## ── Conflicts ─────────────────────────────────── tidymodels_conflicts() ──
## ✖ purrr::discard() masks scales::discard()
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@@ -6175,14 +6175,14 @@
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@@ -6381,14 +6381,14 @@
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censored (development version)
Fixed a bug for proportional_hazards(engine = "glmnet") where prediction didn’t work for a workflow() with a formula as the preprocessor (#264).
extract_fit_engine() now works properly for proportional hazards models fitted with the "glmnet" engine (#266).
survival_time_coxnet() gained a multi argument to allow multiple values for penalty (#278).
censored 0.2.0
CRAN release: 2023-04-13
diff --git a/dev/pkgdown.yml b/dev/pkgdown.yml
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pkgdown_sha: ~
articles:
examples: examples.html
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+last_built: 2023-12-15T14:06Z
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reference: https://censored.tidymodels.org/reference
article: https://censored.tidymodels.org/articles
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diff --git a/dev/reference/blackboost_train.html b/dev/reference/blackboost_train.html
index 9bb76df1..b51de276 100644
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Boosted trees via mboost — blackboost_train • censored Boosted trees via mboost — blackboost_train • censored censored: parsnip Engines for Survival Models — censored-package • censored % slice(1) %>% tidyr::unnest(col = .pred) ## # A tibble: 3 × 2 ## .eval_time .pred_survival ## ## 1 100 0.931 ## 2 500 0.399 ## 3 1000 0.0624"},{"path":"https://censored.tidymodels.org/dev/articles/examples.html","id":"survival_reg-models","dir":"Articles","previous_headings":"","what":"survival_reg() models","title":"Fitting and Predicting with censored","text":"’ll model survival lung cancer patients. can define model specific parameters: Now create model fit object: holdout data can predicted survival probability different time points well event time, linear predictor, quantile, hazard. ’ll model survival lung cancer patients. can define model specific parameters: Now create model fit object: holdout data can predicted survival probability different time points well event time, linear predictor, quantile, hazard. ’ll model survival lung cancer patients. can define model: Now create model fit object: holdout data can predicted survival probability different time points well event time, linear predictor, quantile, hazard.","code":"library(tidymodels) library(censored) tidymodels_prefer() data(cancer) lung <- lung %>% drop_na() lung_train <- lung[-c(1:5), ] lung_test <- lung[1:5, ] sr_spec <- survival_reg(dist = \"weibull\") %>% set_engine(\"survival\") %>% set_mode(\"censored regression\") sr_spec ## Parametric Survival Regression Model Specification (censored regression) ## ## Main Arguments: ## dist = weibull ## ## Computational engine: survival set.seed(1) sr_fit <- sr_spec %>% fit(Surv(time, status) ~ ., data = lung_train) sr_fit ## parsnip model object ## ## Call: ## survival::survreg(formula = Surv(time, status) ~ ., data = data, ## dist = ~\"weibull\", model = TRUE) ## ## Coefficients: ## (Intercept) inst age sex ph.ecog ## 6.2802499155 0.0191302849 -0.0085917372 0.4249655608 -0.5022975982 ## ph.karno pat.karno meal.cal wt.loss ## -0.0085852225 0.0058753359 0.0001003211 0.0127001420 ## ## Scale= 0.6902035 ## ## Loglik(model)= -795.2 Loglik(intercept only)= -811.4 ## Chisq= 32.41 on 8 degrees of freedom, p= 7.85e-05 ## n= 162 predict( sr_fit, lung_test, type = \"survival\", eval_time = c(100, 500, 1000) ) %>% slice(1) %>% tidyr::unnest(col = .pred) ## # A tibble: 3 × 2 ## .eval_time .pred_survival ## ## 1 100 0.912 ## 2 500 0.386 ## 3 1000 0.0742 predict(sr_fit, lung_test, type = \"time\") ## # A tibble: 5 × 1 ## .pred_time ## ## 1 517. ## 2 283. ## 3 361. ## 4 268. ## 5 313. predict(sr_fit, lung_test, type = \"linear_pred\") ## # A tibble: 5 × 1 ## .pred_linear_pred ## ## 1 6.25 ## 2 5.64 ## 3 5.89 ## 4 5.59 ## 5 5.75 predict(sr_fit, lung_test, type = \"quantile\") %>% slice(1) %>% tidyr::unnest(col = .pred) ## # A tibble: 9 × 2 ## .quantile .pred_quantile ## ## 1 0.1 109. ## 2 0.2 184. ## 3 0.3 254. ## 4 0.4 325. ## 5 0.5 401. ## 6 0.6 487. ## 7 0.7 588. ## 8 0.8 718. ## 9 0.9 919. predict(sr_fit, lung_test, type = \"hazard\", eval_time = c(100, 500, 1000)) %>% slice(1) %>% tidyr::unnest(col = .pred) ## # A tibble: 3 × 2 ## .eval_time .pred_hazard ## ## 1 100 0.00134 ## 2 500 0.00276 ## 3 1000 0.00377 library(tidymodels) library(censored) tidymodels_prefer() data(cancer) lung <- lung %>% drop_na() lung_train <- lung[-c(1:5), ] lung_test <- lung[1:5, ] sr_spec <- survival_reg(dist = \"weibull\") %>% set_engine(\"flexsurv\") %>% set_mode(\"censored regression\") sr_spec ## Parametric Survival Regression Model Specification (censored regression) ## ## Main Arguments: ## dist = weibull ## ## Computational engine: flexsurv set.seed(1) sr_fit <- sr_spec %>% fit(Surv(time, status) ~ age + sex + ph.ecog, data = lung_train) sr_fit ## parsnip model object ## ## Call: ## flexsurv::flexsurvreg(formula = Surv(time, status) ~ age + sex + ## ph.ecog, data = data, dist = ~\"weibull\") ## ## Estimates: ## data mean est L95% U95% se exp(est) ## shape NA 1.39e+00 1.21e+00 1.61e+00 1.02e-01 NA ## scale NA 5.74e+02 1.99e+02 1.65e+03 3.10e+02 NA ## age 6.24e+01 -9.02e-03 -2.50e-02 6.93e-03 8.14e-03 9.91e-01 ## sex 1.38e+00 4.02e-01 1.17e-01 6.87e-01 1.45e-01 1.50e+00 ## ph.ecog 9.51e-01 -3.17e-01 -5.13e-01 -1.21e-01 1.00e-01 7.28e-01 ## L95% U95% ## shape NA NA ## scale NA NA ## age 9.75e-01 1.01e+00 ## sex 1.12e+00 1.99e+00 ## ph.ecog 5.99e-01 8.86e-01 ## ## N = 162, Events: 116, Censored: 46 ## Total time at risk: 49401 ## Log-likelihood = -800.356, df = 5 ## AIC = 1610.712 predict( sr_fit, lung_test, type = \"survival\", eval_time = c(100, 500, 1000) ) %>% slice(1) %>% tidyr::unnest(col = .pred) ## # A tibble: 3 × 2 ## .eval_time .pred_survival ## ## 1 100 0.889 ## 2 500 0.330 ## 3 1000 0.0543 predict(sr_fit, lung_test, type = \"time\") ## # A tibble: 5 × 1 ## .pred_time ## ## 1 424. ## 2 341. ## 3 292. ## 4 336. ## 5 327. predict(sr_fit, lung_test, type = \"linear_pred\") ## # A tibble: 5 × 1 ## .pred_linear_pred ## ## 1 6.14 ## 2 5.92 ## 3 5.77 ## 4 5.91 ## 5 5.88 predict(sr_fit, lung_test, type = \"quantile\") %>% slice(1) %>% tidyr::unnest(col = .pred) ## # A tibble: 9 × 2 ## .quantile .pred_quantile ## ## 1 0.1 92.5 ## 2 0.2 158. ## 3 0.3 222. ## 4 0.4 287. ## 5 0.5 357. ## 6 0.6 436. ## 7 0.7 531. ## 8 0.8 653. ## 9 0.9 845. predict(sr_fit, lung_test, type = \"hazard\", eval_time = c(100, 500, 1000)) %>% slice(1) %>% tidyr::unnest(col = .pred) ## # A tibble: 3 × 2 ## .eval_time .pred_hazard ## ## 1 100 0.00164 ## 2 500 0.00309 ## 3 1000 0.00406 library(tidymodels) library(censored) tidymodels_prefer() data(cancer) lung <- lung %>% drop_na() lung_train <- lung[-c(1:5), ] lung_test <- lung[1:5, ] sr_spec <- survival_reg() %>% set_engine(\"flexsurvspline\") %>% set_mode(\"censored regression\") sr_spec ## Parametric Survival Regression Model Specification (censored regression) ## ## Computational engine: flexsurvspline set.seed(1) sr_fit <- sr_spec %>% fit(Surv(time, status) ~ age + sex + ph.ecog, data = lung_train) sr_fit ## parsnip model object ## ## Call: ## flexsurv::flexsurvspline(formula = Surv(time, status) ~ age + ## sex + ph.ecog, data = data) ## ## Estimates: ## data mean est L95% U95% se exp(est) ## gamma0 NA -8.85681 -10.78535 -6.92827 0.98397 NA ## gamma1 NA 1.39431 1.19358 1.59504 0.10241 NA ## age 62.41358 0.01258 -0.00966 0.03482 0.01135 1.01266 ## sex 1.38272 -0.56080 -0.95517 -0.16643 0.20121 0.57075 ## ph.ecog 0.95062 0.44213 0.17197 0.71230 0.13784 1.55602 ## L95% U95% ## gamma0 NA NA ## gamma1 NA NA ## age 0.99039 1.03543 ## sex 0.38475 0.84668 ## ph.ecog 1.18764 2.03867 ## ## N = 162, Events: 116, Censored: 46 ## Total time at risk: 49401 ## Log-likelihood = -800.356, df = 5 ## AIC = 1610.712 predict( sr_fit, lung_test, type = \"survival\", eval_time = c(100, 500, 1000) ) %>% slice(1) %>% tidyr::unnest(col = .pred) ## # A tibble: 3 × 2 ## .eval_time .pred_survival ## ## 1 100 0.889 ## 2 500 0.330 ## 3 1000 0.0543 predict(sr_fit, lung_test, type = \"time\") ## # A tibble: 5 × 1 ## .pred_time ## ## 1 424. ## 2 341. ## 3 292. ## 4 336. ## 5 327. predict(sr_fit, lung_test, type = \"linear_pred\") ## # A tibble: 5 × 1 ## .pred_linear_pred ## ## 1 -8.56 ## 2 -8.26 ## 3 -8.04 ## 4 -8.24 ## 5 -8.20 predict(sr_fit, lung_test, type = \"quantile\") %>% slice(1) %>% tidyr::unnest(col = .pred) ## # A tibble: 9 × 2 ## .quantile .pred_quantile ## ## 1 0.1 92.5 ## 2 0.2 158. ## 3 0.3 222. ## 4 0.4 287. ## 5 0.5 357. ## 6 0.6 436. ## 7 0.7 531. ## 8 0.8 653. ## 9 0.9 845. predict(sr_fit, lung_test, type = \"hazard\", eval_time = c(100, 500, 1000)) %>% slice(1) %>% tidyr::unnest(col = .pred) ## # A tibble: 3 × 2 ## .eval_time .pred_hazard ## ## 1 100 0.00164 ## 2 500 0.00309 ## 3 1000 0.00406"},{"path":"https://censored.tidymodels.org/dev/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Emil Hvitfeldt. Author. Hannah Frick. Author, maintainer. . Copyright holder, funder.","code":""},{"path":"https://censored.tidymodels.org/dev/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Hvitfeldt E, Frick H (2023). censored: 'parsnip' Engines Survival Models. R package version 0.2.0.9001, https://censored.tidymodels.org, https://github.com/tidymodels/censored.","code":"@Manual{, title = {censored: 'parsnip' Engines for Survival Models}, author = {Emil Hvitfeldt and Hannah Frick}, year = {2023}, note = {R package version 0.2.0.9001, https://censored.tidymodels.org}, url = {https://github.com/tidymodels/censored}, }"},{"path":"https://censored.tidymodels.org/dev/index.html","id":"censored-","dir":"","previous_headings":"","what":"parsnip Engines for Survival Models","title":"parsnip Engines for Survival Models","text":"censored parsnip extension package provides engines various models censored regression survival analysis.","code":""},{"path":"https://censored.tidymodels.org/dev/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"parsnip Engines for Survival Models","text":"can install released version censored CRAN : development version GitHub :","code":"install.packages(\"censored\") # install.packages(\"pak\") pak::pak(\"tidymodels/censored\")"},{"path":"https://censored.tidymodels.org/dev/index.html","id":"available-models-engines-and-prediction-types","dir":"","previous_headings":"","what":"Available models, engines, and prediction types","title":"parsnip Engines for Survival Models","text":"censored provides engines models following table. examples, please see Fitting Predicting censored. time event can predicted type = \"time\", survival probability type = \"survival\", linear predictor type = \"linear_pred\", quantiles event time distribution type = \"quantile\", hazard type = \"hazard\".","code":""},{"path":"https://censored.tidymodels.org/dev/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"parsnip Engines for Survival Models","text":"project released Contributor Code Conduct. contributing project, agree abide terms. questions discussions tidymodels packages, modeling, machine learning, please post RStudio Community. think encountered bug, please submit issue. Either way, learn create share reprex (minimal, reproducible example), clearly communicate code. Check details contributing guidelines tidymodels packages get help.","code":""},{"path":"https://censored.tidymodels.org/dev/reference/aorsf_internal.html","id":null,"dir":"Reference","previous_headings":"","what":"Internal helper function for aorsf objects — aorsf_internal","title":"Internal helper function for aorsf objects — aorsf_internal","text":"Internal helper function aorsf objects","code":""},{"path":"https://censored.tidymodels.org/dev/reference/aorsf_internal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Internal helper function for aorsf objects — aorsf_internal","text":"","code":"survival_prob_orsf(object, new_data, eval_time, time = deprecated())"},{"path":"https://censored.tidymodels.org/dev/reference/aorsf_internal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Internal helper function for aorsf objects — aorsf_internal","text":"object model object aorsf::orsf(). new_data data frame predicted. eval_time vector times predict survival probability. time Deprecated favor eval_time. vector times predict survival probability.","code":""},{"path":"https://censored.tidymodels.org/dev/reference/aorsf_internal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Internal helper function for aorsf objects — aorsf_internal","text":"tibble list column nested tibbles.","code":""},{"path":"https://censored.tidymodels.org/dev/reference/aorsf_internal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Internal helper function for aorsf objects — aorsf_internal","text":"","code":"library(aorsf) aorsf <- orsf(na.omit(lung), Surv(time, status) ~ age + ph.ecog, n_tree = 10) preds <- survival_prob_orsf(aorsf, lung[1:3, ], eval_time = c(250, 100))"},{"path":"https://censored.tidymodels.org/dev/reference/blackboost_train.html","id":null,"dir":"Reference","previous_headings":"","what":"Boosted trees via mboost — blackboost_train","title":"Boosted trees via mboost — blackboost_train","text":"blackboost_train() wrapper blackboost() function mboost package fits tree-based models model arguments main function.","code":""},{"path":"https://censored.tidymodels.org/dev/reference/blackboost_train.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Boosted trees via mboost — blackboost_train","text":"","code":"blackboost_train( formula, data, family, weights = NULL, teststat = \"quad\", testtype = \"Teststatistic\", mincriterion = 0, minsplit = 10, minbucket = 4, maxdepth = 2, saveinfo = FALSE, ... )"},{"path":"https://censored.tidymodels.org/dev/reference/blackboost_train.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Boosted trees via mboost — blackboost_train","text":"teststat character specifying type test statistic applied variable selection. testtype character specifying compute distribution test statistic. first three options refer p-values criterion, Teststatistic uses raw statistic criterion. Bonferroni Univariate relate p-values asymptotic distribution (adjusted unadjusted). Bonferroni-adjusted Monte-Carlo p-values computed Bonferroni MonteCarlo given. mincriterion value test statistic 1 - p-value must exceeded order implement split. minsplit minimum sum weights node order considered splitting. minbucket minimum sum weights terminal node. maxdepth maximum depth tree. default maxdepth = Inf means restrictions applied tree sizes. saveinfo logical. Store information variable selection procedure info slot partynode. ... arguments pass. x data frame matrix predictors. y factor vector 2 levels","code":""},{"path":"https://censored.tidymodels.org/dev/reference/blackboost_train.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Boosted trees via mboost — blackboost_train","text":"fitted blackboost model.","code":""},{"path":"https://censored.tidymodels.org/dev/reference/blackboost_train.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Boosted trees via mboost — blackboost_train","text":"","code":"blackboost_train(Surv(time, status) ~ age + ph.ecog, data = lung[-14, ], family = mboost::CoxPH() ) #> #> \t Model-based Boosting #> #> Call: #> mboost::blackboost(formula = formula, data = data, family = family, control = mboost::boost_control(), tree_controls = partykit::ctree_control(teststat = \"quad\", testtype = \"Teststatistic\", mincriterion = 0, minsplit = 10, minbucket = 4, maxdepth = 2, saveinfo = FALSE)) #> #> #> \t Cox Partial Likelihood #> #> Loss function: #> #> Number of boosting iterations: mstop = 100 #> Step size: 0.1 #> Offset: 0 #> Number of baselearners: 1 #>"},{"path":"https://censored.tidymodels.org/dev/reference/censored-package.html","id":null,"dir":"Reference","previous_headings":"","what":"censored: parsnip Engines for Survival Models — censored-package","title":"censored: parsnip Engines for Survival Models — censored-package","text":"censored provides engines survival models parsnip package. models include parametric survival models, proportional hazards models, decision trees, boosted trees, bagged trees, random forests. See \"Fitting Predicting censored\" article various examples. See examples classic survival models fit censored.","code":""},{"path":[]},{"path":"https://censored.tidymodels.org/dev/reference/censored-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"censored: parsnip Engines for Survival Models — censored-package","text":"Maintainer: Hannah Frick hannah@posit.co (ORCID) Authors: Emil Hvitfeldt emil.hvitfeldt@posit.co (ORCID) contributors: Posit Software, PBC [copyright holder, funder]","code":""},{"path":"https://censored.tidymodels.org/dev/reference/censored-package.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"censored: parsnip Engines for Survival Models — censored-package","text":"","code":"# Accelerated Failure Time (AFT) model fit_aft <- survival_reg(dist = \"weibull\") %>% set_engine(\"survival\") %>% fit(Surv(time, status) ~ age + sex + ph.karno, data = lung) predict(fit_aft, lung[1:3, ], type = \"time\") #> # A tibble: 3 × 1 #> .pred_time #> #> 1 355. #> 2 374. #> 3 416. # Cox's Proportional Hazards model fit_cox <- proportional_hazards() %>% set_engine(\"survival\") %>% fit(Surv(time, status) ~ age + sex + ph.karno, data = lung) predict(fit_cox, lung[1:3, ], type = \"time\") #> # A tibble: 3 × 1 #> .pred_time #> #> 1 325. #> 2 343. #> 3 379. # Andersen-Gill model for recurring events fit_ag <- proportional_hazards() %>% set_engine(\"survival\") %>% fit(Surv(tstart, tstop, status) ~ treat + inherit + age + strata(hos.cat), data = cgd ) predict(fit_ag, cgd[1:3, ], type = \"time\") #> # A tibble: 3 × 1 #> .pred_time #> #> 1 319. #> 2 319. #> 3 319."},{"path":"https://censored.tidymodels.org/dev/reference/coxnet_train.html","id":null,"dir":"Reference","previous_headings":"","what":"Wrapper for glmnet for censored — coxnet_train","title":"Wrapper for glmnet for censored — coxnet_train","text":"used directly users.","code":""},{"path":"https://censored.tidymodels.org/dev/reference/coxnet_train.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wrapper for glmnet for censored — coxnet_train","text":"","code":"coxnet_train( formula, data, alpha = 1, lambda = NULL, weights = NULL, ..., call = caller_env() )"},{"path":"https://censored.tidymodels.org/dev/reference/coxnet_train.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wrapper for glmnet for censored — coxnet_train","text":"formula model formula. data data. alpha elasticnet mixing parameter, \\(0\\le\\alpha\\le 1\\). penalty defined $$(1-\\alpha)/2||\\beta||_2^2+\\alpha||\\beta||_1.$$ alpha=1 lasso penalty, alpha=0 ridge penalty. lambda user supplied lambda sequence. Typical usage program compute lambda sequence based nlambda lambda.min.ratio. Supplying value lambda overrides . WARNING: use care. Avoid supplying single value lambda (predictions CV use predict() instead). Supply instead decreasing sequence lambda values. glmnet relies warms starts speed, often faster fit whole path compute single fit. weights observation weights. Can total counts responses proportion matrices. Default 1 observation ... additional parameters passed glmnet::glmnet. call call passed rlang::abort().","code":""},{"path":"https://censored.tidymodels.org/dev/reference/coxnet_train.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wrapper for glmnet for censored — coxnet_train","text":"fitted glmnet model.","code":""},{"path":"https://censored.tidymodels.org/dev/reference/coxnet_train.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Wrapper for glmnet for censored — coxnet_train","text":"wrapper translates formula interface glmnet's matrix due stratification can specified. glmnet requires response stratified via glmnet::stratifySurv(). censored allows specification via survival::strata() term right-hand side formula. formula used generate stratification information needed stratifying response. formula without strata term used generating model matrix glmnet. wrapper retains original formula pre-processing elements including training data allow predictions fitted model.","code":""},{"path":"https://censored.tidymodels.org/dev/reference/coxnet_train.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Wrapper for glmnet for censored — coxnet_train","text":"","code":"coxnet_mod <- coxnet_train(Surv(time, status) ~ age + sex, data = lung)"},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_coxnet.html","id":null,"dir":"Reference","previous_headings":"","what":"A wrapper for survival probabilities with coxnet models — survival_prob_coxnet","title":"A wrapper for survival probabilities with coxnet models — survival_prob_coxnet","text":"wrapper survival probabilities coxnet models","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_coxnet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A wrapper for survival probabilities with coxnet models — survival_prob_coxnet","text":"","code":"survival_prob_coxnet( object, new_data, eval_time, time = deprecated(), output = \"surv\", penalty = NULL, ... )"},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_coxnet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A wrapper for survival probabilities with coxnet models — survival_prob_coxnet","text":"object fitted _coxnet object. new_data Data prediction. eval_time vector integers prediction times. time Deprecated favor eval_time. vector integers prediction times. output One \"surv\" \"haz\". penalty Penalty value(s). ... Options pass survival::survfit().","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_coxnet.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A wrapper for survival probabilities with coxnet models — survival_prob_coxnet","text":"tibble list column nested tibbles.","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_coxnet.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A wrapper for survival probabilities with coxnet models — survival_prob_coxnet","text":"","code":"cox_mod <- proportional_hazards(penalty = 0.1) %>% set_engine(\"glmnet\") %>% fit(Surv(time, status) ~ ., data = lung) survival_prob_coxnet(cox_mod, new_data = lung[1:3, ], eval_time = 300) #> # A tibble: 3 × 1 #> .pred #> #> 1 #> 2 #> 3 "},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_coxph.html","id":null,"dir":"Reference","previous_headings":"","what":"A wrapper for survival probabilities with coxph models — survival_prob_coxph","title":"A wrapper for survival probabilities with coxph models — survival_prob_coxph","text":"wrapper survival probabilities coxph models","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_coxph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A wrapper for survival probabilities with coxph models — survival_prob_coxph","text":"","code":"survival_prob_coxph( x, new_data, eval_time, time = deprecated(), output = \"surv\", interval = \"none\", conf.int = 0.95, ... )"},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_coxph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A wrapper for survival probabilities with coxph models — survival_prob_coxph","text":"x model coxph(). new_data Data prediction eval_time vector integers prediction times. time Deprecated favor eval_time. vector integers prediction times. output One \"surv\", \"conf\", \"haz\". interval Add confidence interval survival probability? Options \"none\" \"confidence\". conf.int confidence level. ... Options pass survival::survfit()","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_coxph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A wrapper for survival probabilities with coxph models — survival_prob_coxph","text":"tibble list column nested tibbles.","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_coxph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A wrapper for survival probabilities with coxph models — survival_prob_coxph","text":"","code":"cox_mod <- coxph(Surv(time, status) ~ ., data = lung) survival_prob_coxph(cox_mod, new_data = lung[1:3, ], eval_time = 300) #> # A tibble: 3 × 1 #> .pred #> #> 1 #> 2 #> 3 "},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_mboost.html","id":null,"dir":"Reference","previous_headings":"","what":"A wrapper for survival probabilities with mboost models — survival_prob_mboost","title":"A wrapper for survival probabilities with mboost models — survival_prob_mboost","text":"wrapper survival probabilities mboost models","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_mboost.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A wrapper for survival probabilities with mboost models — survival_prob_mboost","text":"","code":"survival_prob_mboost(object, new_data, eval_time, time = deprecated())"},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_mboost.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A wrapper for survival probabilities with mboost models — survival_prob_mboost","text":"new_data Data prediction. eval_time vector integers prediction times. time Deprecated favor eval_time. vector integers prediction times. x model blackboost().","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_mboost.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A wrapper for survival probabilities with mboost models — survival_prob_mboost","text":"tibble list column nested tibbles.","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_mboost.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A wrapper for survival probabilities with mboost models — survival_prob_mboost","text":"","code":"library(mboost) #> Loading required package: parallel #> Loading required package: stabs mod <- blackboost(Surv(time, status) ~ ., data = lung, family = CoxPH()) survival_prob_mboost(mod, new_data = lung[1:3, ], eval_time = 300) #> # A tibble: 3 × 1 #> .pred #> #> 1 #> 2 #> 3 "},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_partykit.html","id":null,"dir":"Reference","previous_headings":"","what":"A wrapper for survival probabilities with partykit models — survival_prob_partykit","title":"A wrapper for survival probabilities with partykit models — survival_prob_partykit","text":"wrapper survival probabilities partykit models","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_partykit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A wrapper for survival probabilities with partykit models — survival_prob_partykit","text":"","code":"survival_prob_partykit( object, new_data, eval_time, time = deprecated(), output = \"surv\" )"},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_partykit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A wrapper for survival probabilities with partykit models — survival_prob_partykit","text":"object model object partykit::ctree() partykit::cforest(). new_data data frame predicted. eval_time vector times predict survival probability. time Deprecated favor eval_time. vector times predict survival probability. output Type output. Can either \"surv\" \"haz\".","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_partykit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A wrapper for survival probabilities with partykit models — survival_prob_partykit","text":"tibble list column nested tibbles.","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_partykit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A wrapper for survival probabilities with partykit models — survival_prob_partykit","text":"","code":"library(partykit) #> Loading required package: grid #> Loading required package: libcoin #> Loading required package: mvtnorm #> #> Attaching package: ‘partykit’ #> The following object is masked from ‘package:mboost’: #> #> varimp c_tree <- ctree(Surv(time, status) ~ age + ph.ecog, data = lung) survival_prob_partykit(c_tree, lung[1:3, ], eval_time = 100) #> # A tibble: 3 × 1 #> .pred #> #> 1 #> 2 #> 3 c_forest <- cforest(Surv(time, status) ~ age + ph.ecog, data = lung, ntree = 10) survival_prob_partykit(c_forest, lung[1:3, ], eval_time = 100) #> # A tibble: 3 × 1 #> .pred #> #> 1 #> 2 #> 3 "},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_survbagg.html","id":null,"dir":"Reference","previous_headings":"","what":"A wrapper for survival probabilities with survbagg models — survival_prob_survbagg","title":"A wrapper for survival probabilities with survbagg models — survival_prob_survbagg","text":"wrapper survival probabilities survbagg models","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_survbagg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A wrapper for survival probabilities with survbagg models — survival_prob_survbagg","text":"","code":"survival_prob_survbagg(object, new_data, eval_time, time = deprecated())"},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_survbagg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A wrapper for survival probabilities with survbagg models — survival_prob_survbagg","text":"object model ipred::bagging(). new_data Data prediction. eval_time vector prediction times. time Deprecated favor eval_time. vector prediction times.","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_survbagg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A wrapper for survival probabilities with survbagg models — survival_prob_survbagg","text":"vctrs list tibbles.","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_survbagg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A wrapper for survival probabilities with survbagg models — survival_prob_survbagg","text":"","code":"library(ipred) #> #> Attaching package: ‘ipred’ #> The following object is masked from ‘package:mboost’: #> #> cv bagged_tree <- bagging(Surv(time, status) ~ age + ph.ecog, data = lung) survival_prob_survbagg(bagged_tree, lung[1:3, ], eval_time = 100) #> # A tibble: 3 × 1 #> .pred #> #> 1 #> 2 #> 3 "},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_survreg.html","id":null,"dir":"Reference","previous_headings":"","what":"Internal function helps for parametric survival models — survival_prob_survreg","title":"Internal function helps for parametric survival models — survival_prob_survreg","text":"Internal function helps parametric survival models","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_survreg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Internal function helps for parametric survival models — survival_prob_survreg","text":"","code":"survival_prob_survreg(object, new_data, eval_time, time = deprecated()) hazard_survreg(object, new_data, eval_time)"},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_survreg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Internal function helps for parametric survival models — survival_prob_survreg","text":"object survreg object. new_data data frame. eval_time vector time points. time Deprecated favor eval_time. vector time points.","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_survreg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Internal function helps for parametric survival models — survival_prob_survreg","text":"tibble list column nested tibbles.","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_survreg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Internal function helps for parametric survival models — survival_prob_survreg","text":"","code":"surv_reg <- survreg(Surv(time, status) ~ ., data = lung) survival_prob_survreg(surv_reg, lung[1:3, ], eval_time = 100) #> # A tibble: 3 × 1 #> .pred #> #> 1 #> 2 #> 3 hazard_survreg(surv_reg, lung[1:3, ], eval_time = 100) #> # A tibble: 3 × 1 #> .pred #> #> 1 #> 2 #> 3 "},{"path":"https://censored.tidymodels.org/dev/reference/survival_time_coxnet.html","id":null,"dir":"Reference","previous_headings":"","what":"A wrapper for survival times with coxnet models — survival_time_coxnet","title":"A wrapper for survival times with coxnet models — survival_time_coxnet","text":"wrapper survival times coxnet models","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_time_coxnet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A wrapper for survival times with coxnet models — survival_time_coxnet","text":"","code":"survival_time_coxnet(object, new_data, penalty = NULL, ...)"},{"path":"https://censored.tidymodels.org/dev/reference/survival_time_coxnet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A wrapper for survival times with coxnet models — survival_time_coxnet","text":"object fitted _coxnet object. new_data Data prediction. penalty Penalty value(s). ... Options pass survival::survfit().","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_time_coxnet.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A wrapper for survival times with coxnet models — survival_time_coxnet","text":"vector.","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_time_coxnet.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A wrapper for survival times with coxnet models — survival_time_coxnet","text":"","code":"cox_mod <- proportional_hazards(penalty = 0.1) %>% set_engine(\"glmnet\") %>% fit(Surv(time, status) ~ ., data = lung) survival_time_coxnet(cox_mod, new_data = lung[1:3, ], penalty = 0.1) #> [1] NA 425.4722 NA"},{"path":"https://censored.tidymodels.org/dev/reference/survival_time_coxph.html","id":null,"dir":"Reference","previous_headings":"","what":"A wrapper for survival times with coxph models — survival_time_coxph","title":"A wrapper for survival times with coxph models — survival_time_coxph","text":"wrapper survival times coxph models","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_time_coxph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A wrapper for survival times with coxph models — survival_time_coxph","text":"","code":"survival_time_coxph(object, new_data)"},{"path":"https://censored.tidymodels.org/dev/reference/survival_time_coxph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A wrapper for survival times with coxph models — survival_time_coxph","text":"object model coxph(). new_data Data prediction","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_time_coxph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A wrapper for survival times with coxph models — survival_time_coxph","text":"vector.","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_time_coxph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A wrapper for survival times with coxph models — survival_time_coxph","text":"","code":"cox_mod <- coxph(Surv(time, status) ~ ., data = lung) survival_time_coxph(cox_mod, new_data = lung[1:3, ]) #> [1] NA 470.5813 NA"},{"path":"https://censored.tidymodels.org/dev/reference/survival_time_mboost.html","id":null,"dir":"Reference","previous_headings":"","what":"A wrapper for mean survival times with mboost models — survival_time_mboost","title":"A wrapper for mean survival times with mboost models — survival_time_mboost","text":"wrapper mean survival times mboost models","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_time_mboost.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A wrapper for mean survival times with mboost models — survival_time_mboost","text":"","code":"survival_time_mboost(object, new_data)"},{"path":"https://censored.tidymodels.org/dev/reference/survival_time_mboost.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A wrapper for mean survival times with mboost models — survival_time_mboost","text":"object model blackboost(). new_data Data prediction","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_time_mboost.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A wrapper for mean survival times with mboost models — survival_time_mboost","text":"tibble.","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_time_mboost.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A wrapper for mean survival times with mboost models — survival_time_mboost","text":"","code":"library(mboost) boosted_tree <- blackboost(Surv(time, status) ~ age + ph.ecog, data = lung[-14, ], family = CoxPH() ) survival_time_mboost(boosted_tree, new_data = lung[1:3, ]) #> # A tibble: 3 × 1 #> .pred_time #> #> 1 370. #> 2 337. #> 3 540."},{"path":"https://censored.tidymodels.org/dev/reference/survival_time_survbagg.html","id":null,"dir":"Reference","previous_headings":"","what":"A wrapper for survival times with survbagg models — survival_time_survbagg","title":"A wrapper for survival times with survbagg models — survival_time_survbagg","text":"wrapper survival times survbagg models","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_time_survbagg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A wrapper for survival times with survbagg models — survival_time_survbagg","text":"","code":"survival_time_survbagg(object, new_data)"},{"path":"https://censored.tidymodels.org/dev/reference/survival_time_survbagg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A wrapper for survival times with survbagg models — survival_time_survbagg","text":"object model ipred::bagging(). new_data Data prediction","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_time_survbagg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A wrapper for survival times with survbagg models — survival_time_survbagg","text":"vector.","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_time_survbagg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A wrapper for survival times with survbagg models — survival_time_survbagg","text":"","code":"library(ipred) bagged_tree <- bagging(Surv(time, status) ~ age + ph.ecog, data = lung) survival_time_survbagg(bagged_tree, lung[1:3, ]) #> [1] 363 350 574"},{"path":"https://censored.tidymodels.org/dev/reference/time_to_million.html","id":null,"dir":"Reference","previous_headings":"","what":"Number of days before a movie grosses $1M USD — time_to_million","title":"Number of days before a movie grosses $1M USD — time_to_million","text":"data somewhat biased random sample 551 movies released 2015 2018. Columns include","code":""},{"path":"https://censored.tidymodels.org/dev/reference/time_to_million.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Number of days before a movie grosses $1M USD — time_to_million","text":"time_to_million tibble","code":""},{"path":"https://censored.tidymodels.org/dev/reference/time_to_million.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Number of days before a movie grosses $1M USD — time_to_million","text":"title: character string movie title. time: number days movie earns million US dollars. event: binary value whether movie reached goal. 94% movies observed events. released: date field release date. distributor: factor name distributor. released_theaters: maximum number theaters movie played first two weeks release. year: release year. rated: factor Motion Picture Association film rating. runtime: length movie (minutes). set indicators columns movie genre (e.g. action, crime, etc.). set indicators language (e.g., english, hindi, etc.). set indicators countries movie released (e.g., uk, japan, etc.)","code":""},{"path":"https://censored.tidymodels.org/dev/news/index.html","id":"censored-development-version","dir":"Changelog","previous_headings":"","what":"censored (development version)","title":"censored (development version)","text":"Fixed bug proportional_hazards(engine = \"glmnet\") prediction didn’t work workflow() formula preprocessor (#264). extract_fit_engine() now works properly proportional hazards models fitted \"glmnet\" engine (#266).","code":""},{"path":"https://censored.tidymodels.org/dev/news/index.html","id":"censored-020","dir":"Changelog","previous_headings":"","what":"censored 0.2.0","title":"censored 0.2.0","text":"CRAN release: 2023-04-13","code":""},{"path":"https://censored.tidymodels.org/dev/news/index.html","id":"cross-package-changes-with-parsnip-0-2-0","dir":"Changelog","previous_headings":"","what":"Cross-package changes with parsnip","title":"censored 0.2.0","text":"new eval_time argument replaces time argument time points predict survival probability hazard. time argument deprecated (#244). matrix interface fitting, fit_xy(), now works censored regression models (#225, #234, #247, #251). Improved error messages throughout package (#248).","code":""},{"path":"https://censored.tidymodels.org/dev/news/index.html","id":"new-engines-0-2-0","dir":"Changelog","previous_headings":"","what":"New engines","title":"censored 0.2.0","text":"Added new \"aorsf\" engine rand_forest() accelerated oblique random survival forests aorsf package (@bcjaeger, #211). Added new flexsurvspline engine survival_reg() (@mattwarkentin, #213).","code":""},{"path":"https://censored.tidymodels.org/dev/news/index.html","id":"bug-fixes-0-2-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"censored 0.2.0","text":"Predictions type \"linear_pred\" survival_reg(engine = \"flexsurv\") now correct scale distributions natural scale unrestricted scale location parameter identical, e.g. dist = \"lnorm\" (#229). Predictions type \"linear_pred\" proportional_hazards(engine = \"glmnet\") via multi_predict() now sign via predict() (#242). Predictions survival probability survival_reg(engine = \"flexsurv\") single time point now nested correctly (#254). Predictions survival probability decision_tree(engine = \"rpart\") single observation now work (#256). Predictions type \"quantile\" survival_reg(engine = \"survival\") single observation now work (#257). Fixed bug printing coxnet models, .e., proportional_hazards() models fitted \"glmnet\" engine (#249).","code":""},{"path":"https://censored.tidymodels.org/dev/news/index.html","id":"internal-changes-0-2-0","dir":"Changelog","previous_headings":"","what":"Internal changes","title":"censored 0.2.0","text":"Predictions survival probabilities now calculated via summary.survfit() proportional_hazards() models \"survival\" \"glmnet\" engines, bag_tree() models \"rpart\" engine, decision_tree() models \"partykit\" engines, well rand_forest() models \"partykit\" engine (#221, #224). Added internal survfit_summary_*() helper functions (#216).","code":""},{"path":"https://censored.tidymodels.org/dev/news/index.html","id":"censored-011","dir":"Changelog","previous_headings":"","what":"censored 0.1.1","title":"censored 0.1.1","text":"CRAN release: 2022-09-30 boosted trees \"mboost\" engine, survival probabilities can now predicted time = -Inf. always 1. time = Inf now predicts survival probability 0 (#215). Updated tests model arguments update() methods (#208). Internal re-organisation code (#206, 209). Added NEWS.md file track changes package.","code":""}]
+[{"path":[]},{"path":"https://censored.tidymodels.org/dev/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"members, contributors, leaders pledge make participation community harassment-free experience everyone, regardless age, body size, visible invisible disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, caste, color, religion, sexual identity orientation. pledge act interact ways contribute open, welcoming, diverse, inclusive, healthy community.","code":""},{"path":"https://censored.tidymodels.org/dev/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes positive environment community include: Demonstrating empathy kindness toward people respectful differing opinions, viewpoints, experiences Giving gracefully accepting constructive feedback Accepting responsibility apologizing affected mistakes, learning experience Focusing best just us individuals, overall community Examples unacceptable behavior include: use sexualized language imagery, sexual attention advances kind Trolling, insulting derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical email address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"https://censored.tidymodels.org/dev/CODE_OF_CONDUCT.html","id":"enforcement-responsibilities","dir":"","previous_headings":"","what":"Enforcement Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Community leaders responsible clarifying enforcing standards acceptable behavior take appropriate fair corrective action response behavior deem inappropriate, threatening, offensive, harmful. Community leaders right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, communicate reasons moderation decisions appropriate.","code":""},{"path":"https://censored.tidymodels.org/dev/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within community spaces, also applies individual officially representing community public spaces. Examples representing community include using official e-mail address, posting via official social media account, acting appointed representative online offline event.","code":""},{"path":"https://censored.tidymodels.org/dev/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported community leaders responsible enforcement codeofconduct@posit.co. complaints reviewed investigated promptly fairly. community leaders obligated respect privacy security reporter incident.","code":""},{"path":"https://censored.tidymodels.org/dev/CODE_OF_CONDUCT.html","id":"enforcement-guidelines","dir":"","previous_headings":"","what":"Enforcement Guidelines","title":"Contributor Covenant Code of Conduct","text":"Community leaders follow Community Impact Guidelines determining consequences action deem violation Code Conduct:","code":""},{"path":"https://censored.tidymodels.org/dev/CODE_OF_CONDUCT.html","id":"id_1-correction","dir":"","previous_headings":"Enforcement Guidelines","what":"1. Correction","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Use inappropriate language behavior deemed unprofessional unwelcome community. Consequence: private, written warning community leaders, providing clarity around nature violation explanation behavior inappropriate. public apology may requested.","code":""},{"path":"https://censored.tidymodels.org/dev/CODE_OF_CONDUCT.html","id":"id_2-warning","dir":"","previous_headings":"Enforcement Guidelines","what":"2. Warning","title":"Contributor Covenant Code of Conduct","text":"Community Impact: violation single incident series actions. Consequence: warning consequences continued behavior. interaction people involved, including unsolicited interaction enforcing Code Conduct, specified period time. includes avoiding interactions community spaces well external channels like social media. Violating terms may lead temporary permanent ban.","code":""},{"path":"https://censored.tidymodels.org/dev/CODE_OF_CONDUCT.html","id":"id_3-temporary-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"3. Temporary Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: serious violation community standards, including sustained inappropriate behavior. Consequence: temporary ban sort interaction public communication community specified period time. public private interaction people involved, including unsolicited interaction enforcing Code Conduct, allowed period. Violating terms may lead permanent ban.","code":""},{"path":"https://censored.tidymodels.org/dev/CODE_OF_CONDUCT.html","id":"id_4-permanent-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"4. Permanent Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Demonstrating pattern violation community standards, including sustained inappropriate behavior, harassment individual, aggression toward disparagement classes individuals. Consequence: permanent ban sort public interaction within community.","code":""},{"path":"https://censored.tidymodels.org/dev/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 2.1, available https://www.contributor-covenant.org/version/2/1/code_of_conduct.html. Community Impact Guidelines inspired [Mozilla’s code conduct enforcement ladder][https://github.com/mozilla/inclusion]. answers common questions code conduct, see FAQ https://www.contributor-covenant.org/faq. Translations available https://www.contributor-covenant.org/translations.","code":""},{"path":"https://censored.tidymodels.org/dev/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2023 censored authors Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://censored.tidymodels.org/dev/articles/examples.html","id":"bag_tree-models","dir":"Articles","previous_headings":"","what":"bag_tree() models","title":"Fitting and Predicting with censored","text":"’ll model survival lung cancer patients. can define model specific parameters: Now create model fit object: holdout data can predicted survival probability different time points well event time.","code":"library(tidymodels) ## ── Attaching packages ──────────────────────────────── tidymodels 1.1.1 ── ## ✔ broom 1.0.5 ✔ rsample 1.2.0 ## ✔ dials 1.2.0 ✔ tibble 3.2.1 ## ✔ dplyr 1.1.4 ✔ tidyr 1.3.0 ## ✔ infer 1.0.5 ✔ tune 1.1.2 ## ✔ modeldata 1.2.0 ✔ workflows 1.1.3 ## ✔ parsnip 1.1.1 ✔ workflowsets 1.0.1 ## ✔ purrr 1.0.2 ✔ yardstick 1.2.0 ## ✔ recipes 1.0.9 ## ── Conflicts ─────────────────────────────────── tidymodels_conflicts() ── ## ✖ purrr::discard() masks scales::discard() ## ✖ dplyr::filter() masks stats::filter() ## ✖ dplyr::lag() masks stats::lag() ## ✖ recipes::step() masks stats::step() ## • Learn how to get started at https://www.tidymodels.org/start/ library(censored) ## Loading required package: survival tidymodels_prefer() data(cancer) lung <- lung %>% drop_na() lung_train <- lung[-c(1:5), ] lung_test <- lung[1:5, ] bt_spec <- bag_tree(cost_complexity = 0) %>% set_engine(\"rpart\") %>% set_mode(\"censored regression\") bt_spec ## Bagged Decision Tree Model Specification (censored regression) ## ## Main Arguments: ## cost_complexity = 0 ## min_n = 2 ## ## Computational engine: rpart set.seed(1) bt_fit <- bt_spec %>% fit(Surv(time, status) ~ ., data = lung_train) bt_fit ## parsnip model object ## ## ## Bagging survival trees with 25 bootstrap replications ## ## Call: bagging.data.frame(formula = Surv(time, status) ~ ., data = data) predict( bt_fit, lung_test, type = \"survival\", eval_time = c(100, 500, 1000) ) %>% slice(1) %>% tidyr::unnest(col = .pred) ## # A tibble: 3 × 2 ## .eval_time .pred_survival ## ## 1 100 0.946 ## 2 500 0.333 ## 3 1000 0.00496 predict(bt_fit, lung_test, type = \"time\") ## # A tibble: 5 × 1 ## .pred_time ## ## 1 353 ## 2 293 ## 3 230 ## 4 201 ## 5 268"},{"path":"https://censored.tidymodels.org/dev/articles/examples.html","id":"boost_tree-models","dir":"Articles","previous_headings":"","what":"boost_tree() models","title":"Fitting and Predicting with censored","text":"’ll model survival lung cancer patients. can define model specific parameters: Now create model fit object: holdout data can predicted survival probability different time points well linear predictor.","code":"library(tidymodels) library(censored) tidymodels_prefer() data(cancer) lung <- lung %>% drop_na() lung_train <- lung[-c(1:5), ] lung_test <- lung[1:5, ] bt_spec <- boost_tree(trees = 15) %>% set_engine(\"mboost\") %>% set_mode(\"censored regression\") bt_spec ## Boosted Tree Model Specification (censored regression) ## ## Main Arguments: ## trees = 15 ## ## Computational engine: mboost set.seed(1) bt_fit <- bt_spec %>% fit(Surv(time, status) ~ ., data = lung_train) bt_fit ## parsnip model object ## ## ## Model-based Boosting ## ## Call: ## mboost::blackboost(formula = formula, data = data, family = family, control = mboost::boost_control(mstop = 15), tree_controls = partykit::ctree_control(teststat = \"quad\", testtype = \"Teststatistic\", mincriterion = 0, minsplit = 10, minbucket = 4, maxdepth = 2, saveinfo = FALSE)) ## ## ## Cox Partial Likelihood ## ## Loss function: ## ## Number of boosting iterations: mstop = 15 ## Step size: 0.1 ## Offset: 0 ## Number of baselearners: 1 predict( bt_fit, lung_test, type = \"survival\", eval_time = c(100, 500, 1000) ) %>% slice(1) %>% tidyr::unnest(col = .pred) ## # A tibble: 3 × 2 ## .eval_time .pred_survival ## ## 1 100 0.867 ## 2 500 0.294 ## 3 1000 0.0441 predict(bt_fit, lung_test, type = \"linear_pred\") ## # A tibble: 5 × 1 ## .pred_linear_pred ## ## 1 0.0823 ## 2 -0.455 ## 3 0.0661 ## 4 -0.724 ## 5 -0.724"},{"path":"https://censored.tidymodels.org/dev/articles/examples.html","id":"decision_tree-models","dir":"Articles","previous_headings":"","what":"decision_tree() models","title":"Fitting and Predicting with censored","text":"’ll model survival lung cancer patients. can define model specific parameters: Now create model fit object: holdout data can predicted survival probability different time points well event time. ’ll model survival lung cancer patients. can define model specific parameters: Now create model fit object: holdout data can predicted survival probability different time points well event time.","code":"library(tidymodels) library(censored) tidymodels_prefer() data(cancer) lung <- lung %>% drop_na() lung_train <- lung[-c(1:5), ] lung_test <- lung[1:5, ] dt_spec <- decision_tree(cost_complexity = 0) %>% set_engine(\"rpart\") %>% set_mode(\"censored regression\") dt_spec ## Decision Tree Model Specification (censored regression) ## ## Main Arguments: ## cost_complexity = 0 ## ## Computational engine: rpart set.seed(1) dt_fit <- dt_spec %>% fit(Surv(time, status) ~ ., data = lung_train) dt_fit ## parsnip model object ## ## $rpart ## n= 162 ## ## node), split, n, deviance, yval ## * denotes terminal node ## ## 1) root 162 217.089100 1.0000000 ## 2) ph.ecog< 1.5 125 146.610800 0.8606149 ## 4) pat.karno>=65 117 134.248900 0.8042241 ## 8) sex>=1.5 47 58.371280 0.5920010 ## 16) inst>=12.5 16 17.696750 0.3469493 * ## 17) inst< 12.5 31 36.986020 0.7601739 ## 34) ph.ecog< 0.5 14 21.869860 0.4765888 * ## 35) ph.ecog>=0.5 17 12.197510 0.9977683 * ## 9) sex< 1.5 70 71.035080 0.9843711 ## 18) wt.loss< -0.5 10 7.608541 0.6466464 * ## 19) wt.loss>=-0.5 60 61.204860 1.0855380 ## 38) inst< 18.5 51 52.890560 0.9994210 ## 76) pat.karno< 85 27 30.835530 0.8204259 ## 152) age< 65.5 16 16.499450 0.6396414 * ## 153) age>=65.5 11 12.211210 1.2318540 * ## 77) pat.karno>=85 24 20.327560 1.2436570 ## 154) pat.karno>=95 10 6.634957 0.7568023 * ## 155) pat.karno< 95 14 10.631990 1.6387150 * ## 39) inst>=18.5 9 6.360874 1.6566500 * ## 5) pat.karno< 65 8 5.011986 2.2376180 * ## 3) ph.ecog>=1.5 37 59.992750 1.7157640 ## 6) wt.loss>=21 10 10.703230 0.6678083 * ## 7) wt.loss< 21 27 29.918520 3.1500170 ## 14) sex>=1.5 12 7.395091 1.9066160 * ## 15) sex< 1.5 15 16.563010 4.5917120 * ## ## $survfit ## ## Call: prodlim::prodlim(formula = form, data = data) ## Stratified Kaplan-Meier estimator for the conditional event time survival function ## Discrete predictor variable: rpartFactor (0.34694933272507, 0.47658881486553, 0.639641354557786, 0.646646427745816, 0.667808261569019, 0.756802251840104, 0.997768280401696, 1.23185367065451, 1.638714591616, 1.65664969973098, 1.90661557969861, 2.23761769770399, 4.59171172488878) ## ## Right-censored response of a survival model ## ## No.Observations: 162 ## ## Pattern: ## Freq ## event 116 ## right.censored 46 ## ## $levels ## [1] \"0.34694933272507\" \"0.47658881486553\" \"0.639641354557786\" ## [4] \"0.646646427745816\" \"0.667808261569019\" \"0.756802251840104\" ## [7] \"0.997768280401696\" \"1.23185367065451\" \"1.638714591616\" ## [10] \"1.65664969973098\" \"1.90661557969861\" \"2.23761769770399\" ## [13] \"4.59171172488878\" ## ## attr(,\"class\") ## [1] \"pecRpart\" predict( dt_fit, lung_test, type = \"survival\", eval_time = c(100, 500, 1000) ) %>% slice(1) %>% tidyr::unnest(col = .pred) ## # A tibble: 3 × 2 ## .eval_time .pred_survival ## ## 1 100 0.786 ## 2 500 0.143 ## 3 1000 NA predict(dt_fit, lung_test, type = \"time\") ## # A tibble: 5 × 1 ## .pred_time ## ## 1 1.64 ## 2 2.24 ## 3 1.23 ## 4 1.91 ## 5 1.91 library(tidymodels) library(censored) tidymodels_prefer() data(cancer) lung <- lung %>% drop_na() lung_train <- lung[-c(1:5), ] lung_test <- lung[1:5, ] dt_spec <- decision_tree() %>% set_engine(\"partykit\") %>% set_mode(\"censored regression\") dt_spec ## Decision Tree Model Specification (censored regression) ## ## Computational engine: partykit set.seed(1) dt_fit <- dt_spec %>% fit(Surv(time, status) ~ ., data = lung_train) dt_fit ## parsnip model object ## ## ## Model formula: ## Surv(time, status) ~ inst + age + sex + ph.ecog + ph.karno + ## pat.karno + meal.cal + wt.loss ## ## Fitted party: ## [1] root ## | [2] ph.ecog <= 1: 363.000 (n = 125) ## | [3] ph.ecog > 1 ## | | [4] wt.loss <= 20 ## | | | [5] sex <= 1: 65.000 (n = 15) ## | | | [6] sex > 1: 201.000 (n = 12) ## | | [7] wt.loss > 20: 524.000 (n = 10) ## ## Number of inner nodes: 3 ## Number of terminal nodes: 4 predict( dt_fit, lung_test, type = \"survival\", eval_time = c(100, 500, 1000) ) %>% slice(1) %>% tidyr::unnest(col = .pred) ## # A tibble: 3 × 2 ## .eval_time .pred_survival ## ## 1 100 0.896 ## 2 500 0.334 ## 3 1000 0.0719 predict(dt_fit, lung_test, type = \"time\") ## # A tibble: 5 × 1 ## .pred_time ## ## 1 363 ## 2 363 ## 3 363 ## 4 201 ## 5 201"},{"path":"https://censored.tidymodels.org/dev/articles/examples.html","id":"proportional_hazards-models","dir":"Articles","previous_headings":"","what":"proportional_hazards() models","title":"Fitting and Predicting with censored","text":"’ll model survival lung cancer patients. can define model specific parameters: Now create model fit object: holdout data can predicted survival probability different time points well linear predictor event time. ’ll model survival lung cancer patients. can define model specific parameters: Now create model fit object: holdout data can predicted survival probability different time points well linear predictor.","code":"library(tidymodels) library(censored) tidymodels_prefer() data(cancer) lung <- lung %>% drop_na() lung_train <- lung[-c(1:5), ] lung_test <- lung[1:5, ] ph_spec <- proportional_hazards() %>% set_engine(\"survival\") %>% set_mode(\"censored regression\") ph_spec ## Proportional Hazards Model Specification (censored regression) ## ## Computational engine: survival set.seed(1) ph_fit <- ph_spec %>% fit(Surv(time, status) ~ ., data = lung_train) ph_fit ## parsnip model object ## ## Call: ## survival::coxph(formula = Surv(time, status) ~ ., data = data, ## model = TRUE, x = TRUE) ## ## coef exp(coef) se(coef) z p ## inst -0.0291726 0.9712488 0.0131293 -2.222 0.02629 ## age 0.0146341 1.0147417 0.0119705 1.223 0.22151 ## sex -0.5977137 0.5500678 0.2051326 -2.914 0.00357 ## ph.ecog 0.7507039 2.1184906 0.2536100 2.960 0.00308 ## ph.karno 0.0137315 1.0138262 0.0132752 1.034 0.30096 ## pat.karno -0.0082098 0.9918238 0.0082560 -0.994 0.32002 ## meal.cal -0.0001233 0.9998767 0.0002841 -0.434 0.66435 ## wt.loss -0.0188464 0.9813301 0.0082051 -2.297 0.02162 ## ## Likelihood ratio test=32.61 on 8 df, p=7.224e-05 ## n= 162, number of events= 116 predict( ph_fit, lung_test, type = \"survival\", eval_time = c(100, 500, 1000) ) %>% slice(1) %>% tidyr::unnest(col = .pred) ## # A tibble: 3 × 2 ## .eval_time .pred_survival ## ## 1 100 0.903 ## 2 500 0.410 ## 3 1000 0.0953 predict(ph_fit, lung_test, type = \"linear_pred\") ## # A tibble: 5 × 1 ## .pred_linear_pred ## ## 1 -0.373 ## 2 -1.24 ## 3 -0.852 ## 4 -1.33 ## 5 -1.11 predict(ph_fit, lung_test, type = \"time\") ## # A tibble: 5 × 1 ## .pred_time ## ## 1 448. ## 2 262. ## 3 337. ## 4 246. ## 5 286. library(tidymodels) library(censored) tidymodels_prefer() data(cancer) lung <- lung %>% drop_na() lung_train <- lung[-c(1:5), ] lung_test <- lung[1:5, ] ph_spec <- proportional_hazards(penalty = 0.1) %>% set_engine(\"glmnet\") %>% set_mode(\"censored regression\") ph_spec ## Proportional Hazards Model Specification (censored regression) ## ## Main Arguments: ## penalty = 0.1 ## ## Computational engine: glmnet set.seed(1) ph_fit <- ph_spec %>% fit(Surv(time, status) ~ ., data = lung_train) ph_fit ## parsnip model object ## ## Fit time: NA ## ## Call: glmnet::glmnet(x = data_obj$x, y = data_obj$y, family = \"cox\", weights = weights, alpha = alpha, lambda = lambda) ## ## Df %Dev Lambda ## 1 0 0.00 0.221000 ## 2 1 0.23 0.201400 ## 3 2 0.43 0.183500 ## 4 2 0.72 0.167200 ## 5 2 0.96 0.152300 ## 6 2 1.17 0.138800 ## 7 2 1.33 0.126500 ## 8 3 1.48 0.115200 ## 9 4 1.61 0.105000 ## 10 4 1.74 0.095660 ## 11 5 1.87 0.087160 ## 12 6 2.02 0.079420 ## 13 6 2.22 0.072370 ## 14 6 2.40 0.065940 ## 15 6 2.54 0.060080 ## 16 6 2.66 0.054740 ## 17 6 2.77 0.049880 ## 18 6 2.85 0.045450 ## 19 6 2.92 0.041410 ## 20 6 2.98 0.037730 ## 21 7 3.04 0.034380 ## 22 7 3.08 0.031330 ## 23 7 3.12 0.028540 ## 24 7 3.16 0.026010 ## 25 7 3.19 0.023700 ## 26 7 3.21 0.021590 ## 27 8 3.23 0.019670 ## 28 8 3.27 0.017930 ## 29 8 3.30 0.016330 ## 30 8 3.32 0.014880 ## 31 8 3.34 0.013560 ## 32 8 3.36 0.012360 ## 33 8 3.37 0.011260 ## 34 8 3.39 0.010260 ## 35 8 3.40 0.009346 ## 36 8 3.40 0.008516 ## 37 8 3.41 0.007760 ## 38 8 3.42 0.007070 ## 39 8 3.42 0.006442 ## 40 8 3.43 0.005870 ## 41 8 3.43 0.005348 ## 42 8 3.43 0.004873 ## 43 8 3.43 0.004440 ## 44 8 3.44 0.004046 ## 45 8 3.44 0.003686 ## 46 8 3.44 0.003359 ## 47 8 3.44 0.003061 ## 48 8 3.44 0.002789 ## 49 8 3.44 0.002541 ## 50 8 3.44 0.002315 ## The training data has been saved for prediction. predict( ph_fit, lung_test, type = \"survival\", eval_time = c(100, 500, 1000) ) %>% slice(1) %>% tidyr::unnest(col = .pred) ## # A tibble: 3 × 2 ## .eval_time .pred_survival ## ## 1 100 0.874 ## 2 500 0.349 ## 3 1000 0.0804 predict(ph_fit, lung_test, type = \"linear_pred\") ## # A tibble: 5 × 1 ## .pred_linear_pred ## ## 1 0.272 ## 2 0.0000798 ## 3 0.00575 ## 4 -0.0211 ## 5 -0.00345"},{"path":"https://censored.tidymodels.org/dev/articles/examples.html","id":"rand_forest-models","dir":"Articles","previous_headings":"","what":"rand_forest() models","title":"Fitting and Predicting with censored","text":"’ll model survival lung cancer patients. can define model specific parameters: Now create model fit object: holdout data can predicted survival probability different time points well event time. ’ll model survival lung cancer patients. can define model specific parameters: Now create model fit object: holdout data can predicted survival probability different time points well event time.","code":"library(tidymodels) library(censored) tidymodels_prefer() data(cancer) lung <- lung %>% drop_na() lung_train <- lung[-c(1:5), ] lung_test <- lung[1:5, ] rf_spec <- rand_forest(trees = 200) %>% set_engine(\"partykit\") %>% set_mode(\"censored regression\") rf_spec ## Random Forest Model Specification (censored regression) ## ## Main Arguments: ## trees = 200 ## ## Computational engine: partykit set.seed(1) rf_fit <- rf_spec %>% fit(Surv(time, status) ~ ., data = lung_train) rf_fit ## parsnip model object ## ## $nodes ## $nodes[[1]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V5 <= 1 ## | | | [4] V3 <= 64 ## | | | | [5] V8 <= 1025 * ## | | | | [6] V8 > 1025 * ## | | | [7] V3 > 64 * ## | | [8] V5 > 1 * ## | [9] V4 > 1 ## | | [10] V5 <= 1 ## | | | [11] V5 <= 0 * ## | | | [12] V5 > 0 * ## | | [13] V5 > 1 * ## ## $nodes[[2]] ## [1] root ## | [2] V5 <= 0 ## | | [3] V4 <= 1 * ## | | [4] V4 > 1 * ## | [5] V5 > 0 ## | | [6] V5 <= 1 ## | | | [7] V9 <= 19 ## | | | | [8] V4 <= 1 ## | | | | | [9] V9 <= 6 * ## | | | | | [10] V9 > 6 * ## | | | | [11] V4 > 1 * ## | | | [12] V9 > 19 * ## | | [13] V5 > 1 ## | | | [14] V4 <= 1 * ## | | | [15] V4 > 1 * ## ## $nodes[[3]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V5 <= 0 ## | | | [4] V2 <= 5 * ## | | | [5] V2 > 5 ## | | | | [6] V6 <= 90 * ## | | | | [7] V6 > 90 * ## | | [8] V5 > 0 ## | | | [9] V6 <= 80 ## | | | | [10] V7 <= 70 * ## | | | | [11] V7 > 70 ## | | | | | [12] V2 <= 10 * ## | | | | | [13] V2 > 10 * ## | | | [14] V6 > 80 * ## | [15] V5 > 1 ## | | [16] V6 <= 60 * ## | | [17] V6 > 60 * ## ## $nodes[[4]] ## [1] root ## | [2] V5 <= 0 ## | | [3] V7 <= 80 * ## | | [4] V7 > 80 * ## | [5] V5 > 0 ## | | [6] V4 <= 1 ## | | | [7] V6 <= 80 ## | | | | [8] V3 <= 65 * ## | | | | [9] V3 > 65 ## | | | | | [10] V9 <= 7 * ## | | | | | [11] V9 > 7 * ## | | | [12] V6 > 80 * ## | | [13] V4 > 1 ## | | | [14] V5 <= 1 * ## | | | [15] V5 > 1 * ## ## $nodes[[5]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V4 <= 1 ## | | | [4] V6 <= 80 ## | | | | [5] V7 <= 80 * ## | | | | [6] V7 > 80 * ## | | | [7] V6 > 80 ## | | | | [8] V9 <= 12 ## | | | | | [9] V2 <= 11 * ## | | | | | [10] V2 > 11 * ## | | | | [11] V9 > 12 * ## | | [12] V4 > 1 ## | | | [13] V3 <= 53 * ## | | | [14] V3 > 53 ## | | | | [15] V3 <= 64 * ## | | | | [16] V3 > 64 * ## | [17] V5 > 1 ## | | [18] V8 <= 925 * ## | | [19] V8 > 925 * ## ## $nodes[[6]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V6 <= 80 ## | | | [4] V8 <= 613 * ## | | | [5] V8 > 613 ## | | | | [6] V2 <= 10 * ## | | | | [7] V2 > 10 * ## | | [8] V6 > 80 ## | | | [9] V8 <= 875 * ## | | | [10] V8 > 875 ## | | | | [11] V9 <= 2 * ## | | | | [12] V9 > 2 * ## | [13] V4 > 1 ## | | [14] V6 <= 70 * ## | | [15] V6 > 70 ## | | | [16] V2 <= 11 * ## | | | [17] V2 > 11 * ## ## $nodes[[7]] ## [1] root ## | [2] V7 <= 60 * ## | [3] V7 > 60 ## | | [4] V3 <= 74 ## | | | [5] V7 <= 90 ## | | | | [6] V5 <= 0 * ## | | | | [7] V5 > 0 ## | | | | | [8] V7 <= 70 * ## | | | | | [9] V7 > 70 ## | | | | | | [10] V9 <= 4 * ## | | | | | | [11] V9 > 4 * ## | | | [12] V7 > 90 * ## | | [13] V3 > 74 * ## ## $nodes[[8]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V3 <= 64 ## | | | [4] V4 <= 1 ## | | | | [5] V5 <= 0 * ## | | | | [6] V5 > 0 * ## | | | [7] V4 > 1 ## | | | | [8] V9 <= 6 * ## | | | | [9] V9 > 6 * ## | | [10] V3 > 64 ## | | | [11] V7 <= 80 * ## | | | [12] V7 > 80 * ## | [13] V5 > 1 ## | | [14] V4 <= 1 * ## | | [15] V4 > 1 * ## ## $nodes[[9]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V6 <= 80 ## | | | [4] V9 <= 20 ## | | | | [5] V6 <= 70 * ## | | | | [6] V6 > 70 * ## | | | [7] V9 > 20 * ## | | [8] V6 > 80 ## | | | [9] V7 <= 80 * ## | | | [10] V7 > 80 * ## | [11] V4 > 1 ## | | [12] V7 <= 90 ## | | | [13] V9 <= 3 * ## | | | [14] V9 > 3 * ## | | [15] V7 > 90 * ## ## $nodes[[10]] ## [1] root ## | [2] V5 <= 0 ## | | [3] V3 <= 64 ## | | | [4] V9 <= 3 * ## | | | [5] V9 > 3 * ## | | [6] V3 > 64 * ## | [7] V5 > 0 ## | | [8] V9 <= 27 ## | | | [9] V5 <= 1 ## | | | | [10] V9 <= 14 ## | | | | | [11] V4 <= 1 * ## | | | | | [12] V4 > 1 * ## | | | | [13] V9 > 14 * ## | | | [14] V5 > 1 * ## | | [15] V9 > 27 * ## ## $nodes[[11]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V7 <= 90 ## | | | [4] V7 <= 70 * ## | | | [5] V7 > 70 ## | | | | [6] V3 <= 70 ## | | | | | [7] V7 <= 80 * ## | | | | | [8] V7 > 80 ## | | | | | | [9] V3 <= 61 * ## | | | | | | [10] V3 > 61 * ## | | | | [11] V3 > 70 * ## | | [12] V7 > 90 * ## | [13] V5 > 1 ## | | [14] V4 <= 1 * ## | | [15] V4 > 1 * ## ## $nodes[[12]] ## [1] root ## | [2] V2 <= 21 ## | | [3] V7 <= 60 * ## | | [4] V7 > 60 ## | | | [5] V6 <= 70 * ## | | | [6] V6 > 70 ## | | | | [7] V7 <= 90 ## | | | | | [8] V5 <= 0 * ## | | | | | [9] V5 > 0 ## | | | | | | [10] V9 <= 14 ## | | | | | | | [11] V7 <= 80 * ## | | | | | | | [12] V7 > 80 * ## | | | | | | [13] V9 > 14 * ## | | | | [14] V7 > 90 * ## | [15] V2 > 21 * ## ## $nodes[[13]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V7 <= 60 * ## | | [4] V7 > 60 ## | | | [5] V5 <= 0 * ## | | | [6] V5 > 0 ## | | | | [7] V3 <= 60 * ## | | | | [8] V3 > 60 ## | | | | | [9] V7 <= 70 * ## | | | | | [10] V7 > 70 * ## | [11] V4 > 1 ## | | [12] V5 <= 0 * ## | | [13] V5 > 0 ## | | | [14] V5 <= 1 * ## | | | [15] V5 > 1 * ## ## $nodes[[14]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V5 <= 1 ## | | | [4] V3 <= 53 * ## | | | [5] V3 > 53 ## | | | | [6] V9 <= 14 ## | | | | | [7] V9 <= 2 * ## | | | | | [8] V9 > 2 * ## | | | | [9] V9 > 14 * ## | | [10] V5 > 1 * ## | [11] V4 > 1 ## | | [12] V6 <= 80 ## | | | [13] V5 <= 1 * ## | | | [14] V5 > 1 * ## | | [15] V6 > 80 ## | | | [16] V5 <= 0 * ## | | | [17] V5 > 0 * ## ## $nodes[[15]] ## [1] root ## | [2] V7 <= 60 * ## | [3] V7 > 60 ## | | [4] V5 <= 1 ## | | | [5] V4 <= 1 ## | | | | [6] V8 <= 1275 ## | | | | | [7] V3 <= 59 * ## | | | | | [8] V3 > 59 ## | | | | | | [9] V5 <= 0 * ## | | | | | | [10] V5 > 0 * ## | | | | [11] V8 > 1275 * ## | | | [12] V4 > 1 ## | | | | [13] V6 <= 90 ## | | | | | [14] V8 <= 875 * ## | | | | | [15] V8 > 875 * ## | | | | [16] V6 > 90 * ## | | [17] V5 > 1 * ## ## $nodes[[16]] ## [1] root ## | [2] V7 <= 60 ## | | [3] V9 <= 8 * ## | | [4] V9 > 8 * ## | [5] V7 > 60 ## | | [6] V5 <= 1 ## | | | [7] V5 <= 0 ## | | | | [8] V6 <= 90 * ## | | | | [9] V6 > 90 * ## | | | [10] V5 > 0 ## | | | | [11] V4 <= 1 ## | | | | | [12] V6 <= 80 * ## | | | | | [13] V6 > 80 * ## | | | | [14] V4 > 1 * ## | | [15] V5 > 1 * ## ## $nodes[[17]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V4 <= 1 ## | | | [4] V9 <= 10 ## | | | | [5] V5 <= 0 * ## | | | | [6] V5 > 0 * ## | | | [7] V9 > 10 * ## | | [8] V4 > 1 ## | | | [9] V6 <= 80 * ## | | | [10] V6 > 80 * ## | [11] V5 > 1 ## | | [12] V9 <= 10 * ## | | [13] V9 > 10 * ## ## $nodes[[18]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V3 <= 52 * ## | | [4] V3 > 52 ## | | | [5] V5 <= 0 ## | | | | [6] V6 <= 90 * ## | | | | [7] V6 > 90 * ## | | | [8] V5 > 0 ## | | | | [9] V4 <= 1 ## | | | | | [10] V3 <= 66 * ## | | | | | [11] V3 > 66 * ## | | | | [12] V4 > 1 * ## | [13] V5 > 1 ## | | [14] V9 <= 11 * ## | | [15] V9 > 11 * ## ## $nodes[[19]] ## [1] root ## | [2] V5 <= 0 ## | | [3] V9 <= 3 * ## | | [4] V9 > 3 * ## | [5] V5 > 0 ## | | [6] V9 <= 27 ## | | | [7] V5 <= 1 ## | | | | [8] V7 <= 80 ## | | | | | [9] V7 <= 70 * ## | | | | | [10] V7 > 70 * ## | | | | [11] V7 > 80 * ## | | | [12] V5 > 1 * ## | | [13] V9 > 27 * ## ## $nodes[[20]] ## [1] root ## | [2] V7 <= 70 ## | | [3] V9 <= 24 ## | | | [4] V6 <= 70 * ## | | | [5] V6 > 70 * ## | | [6] V9 > 24 * ## | [7] V7 > 70 ## | | [8] V4 <= 1 ## | | | [9] V5 <= 0 * ## | | | [10] V5 > 0 ## | | | | [11] V9 <= 1 * ## | | | | [12] V9 > 1 * ## | | [13] V4 > 1 ## | | | [14] V7 <= 90 * ## | | | [15] V7 > 90 * ## ## $nodes[[21]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V3 <= 64 ## | | | [4] V8 <= 1060 ## | | | | [5] V5 <= 0 * ## | | | | [6] V5 > 0 * ## | | | [7] V8 > 1060 ## | | | | [8] V6 <= 90 * ## | | | | [9] V6 > 90 * ## | | [10] V3 > 64 ## | | | [11] V7 <= 80 * ## | | | [12] V7 > 80 * ## | [13] V5 > 1 ## | | [14] V9 <= 20 * ## | | [15] V9 > 20 * ## ## $nodes[[22]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V3 <= 64 ## | | | [4] V5 <= 0 * ## | | | [5] V5 > 0 ## | | | | [6] V4 <= 1 ## | | | | | [7] V9 <= 10 * ## | | | | | [8] V9 > 10 * ## | | | | [9] V4 > 1 * ## | | [10] V3 > 64 ## | | | [11] V6 <= 80 * ## | | | [12] V6 > 80 * ## | [13] V5 > 1 ## | | [14] V9 <= 11 * ## | | [15] V9 > 11 * ## ## $nodes[[23]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V9 <= 20 ## | | | [4] V6 <= 70 * ## | | | [5] V6 > 70 ## | | | | [6] V2 <= 4 * ## | | | | [7] V2 > 4 ## | | | | | [8] V9 <= 5 * ## | | | | | [9] V9 > 5 * ## | | [10] V9 > 20 * ## | [11] V4 > 1 ## | | [12] V5 <= 0 * ## | | [13] V5 > 0 ## | | | [14] V2 <= 12 * ## | | | [15] V2 > 12 * ## ## $nodes[[24]] ## [1] root ## | [2] V7 <= 60 ## | | [3] V9 <= 13 * ## | | [4] V9 > 13 * ## | [5] V7 > 60 ## | | [6] V3 <= 64 ## | | | [7] V8 <= 1150 ## | | | | [8] V8 <= 925 ## | | | | | [9] V8 <= 768 * ## | | | | | [10] V8 > 768 * ## | | | | [11] V8 > 925 * ## | | | [12] V8 > 1150 * ## | | [13] V3 > 64 ## | | | [14] V7 <= 80 * ## | | | [15] V7 > 80 * ## ## $nodes[[25]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V5 <= 1 ## | | | [4] V7 <= 70 * ## | | | [5] V7 > 70 ## | | | | [6] V5 <= 0 * ## | | | | [7] V5 > 0 * ## | | [8] V5 > 1 * ## | [9] V4 > 1 ## | | [10] V9 <= 3 * ## | | [11] V9 > 3 * ## ## $nodes[[26]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V5 <= 0 ## | | | [4] V3 <= 64 ## | | | | [5] V7 <= 90 * ## | | | | [6] V7 > 90 * ## | | | [7] V3 > 64 * ## | | [8] V5 > 0 ## | | | [9] V6 <= 80 ## | | | | [10] V2 <= 13 ## | | | | | [11] V7 <= 70 * ## | | | | | [12] V7 > 70 * ## | | | | [13] V2 > 13 * ## | | | [14] V6 > 80 * ## | [15] V5 > 1 ## | | [16] V9 <= 20 * ## | | [17] V9 > 20 * ## ## $nodes[[27]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V5 <= 1 ## | | | [4] V7 <= 80 ## | | | | [5] V3 <= 66 * ## | | | | [6] V3 > 66 * ## | | | [7] V7 > 80 ## | | | | [8] V8 <= 1025 * ## | | | | [9] V8 > 1025 * ## | | [10] V5 > 1 * ## | [11] V4 > 1 ## | | [12] V9 <= -1 * ## | | [13] V9 > -1 ## | | | [14] V6 <= 70 * ## | | | [15] V6 > 70 ## | | | | [16] V7 <= 80 * ## | | | | [17] V7 > 80 * ## ## $nodes[[28]] ## [1] root ## | [2] V7 <= 90 ## | | [3] V5 <= 1 ## | | | [4] V4 <= 1 ## | | | | [5] V8 <= 1225 ## | | | | | [6] V8 <= 463 * ## | | | | | [7] V8 > 463 ## | | | | | | [8] V6 <= 80 * ## | | | | | | [9] V6 > 80 * ## | | | | [10] V8 > 1225 * ## | | | [11] V4 > 1 ## | | | | [12] V9 <= 4 * ## | | | | [13] V9 > 4 * ## | | [14] V5 > 1 ## | | | [15] V4 <= 1 * ## | | | [16] V4 > 1 * ## | [17] V7 > 90 * ## ## $nodes[[29]] ## [1] root ## | [2] V7 <= 90 ## | | [3] V8 <= 675 ## | | | [4] V7 <= 80 * ## | | | [5] V7 > 80 * ## | | [6] V8 > 675 ## | | | [7] V7 <= 60 * ## | | | [8] V7 > 60 ## | | | | [9] V3 <= 64 ## | | | | | [10] V5 <= 0 * ## | | | | | [11] V5 > 0 ## | | | | | | [12] V8 <= 975 * ## | | | | | | [13] V8 > 975 * ## | | | | [14] V3 > 64 * ## | [15] V7 > 90 * ## ## $nodes[[30]] ## [1] root ## | [2] V7 <= 60 * ## | [3] V7 > 60 ## | | [4] V9 <= 18 ## | | | [5] V4 <= 1 ## | | | | [6] V2 <= 11 * ## | | | | [7] V2 > 11 * ## | | | [8] V4 > 1 ## | | | | [9] V6 <= 90 ## | | | | | [10] V5 <= 0 * ## | | | | | [11] V5 > 0 ## | | | | | | [12] V3 <= 56 * ## | | | | | | [13] V3 > 56 * ## | | | | [14] V6 > 90 * ## | | [15] V9 > 18 * ## ## $nodes[[31]] ## [1] root ## | [2] V7 <= 80 ## | | [3] V4 <= 1 ## | | | [4] V3 <= 65 * ## | | | [5] V3 > 65 * ## | | [6] V4 > 1 ## | | | [7] V2 <= 10 * ## | | | [8] V2 > 10 * ## | [9] V7 > 80 ## | | [10] V5 <= 0 * ## | | [11] V5 > 0 ## | | | [12] V2 <= 13 * ## | | | [13] V2 > 13 * ## ## $nodes[[32]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V5 <= 1 ## | | | [4] V8 <= 575 * ## | | | [5] V8 > 575 ## | | | | [6] V2 <= 16 ## | | | | | [7] V3 <= 60 * ## | | | | | [8] V3 > 60 * ## | | | | [9] V2 > 16 * ## | | [10] V5 > 1 * ## | [11] V4 > 1 ## | | [12] V7 <= 80 ## | | | [13] V9 <= 3 * ## | | | [14] V9 > 3 * ## | | [15] V7 > 80 * ## ## $nodes[[33]] ## [1] root ## | [2] V6 <= 70 ## | | [3] V9 <= 2 * ## | | [4] V9 > 2 * ## | [5] V6 > 70 ## | | [6] V9 <= 3 ## | | | [7] V4 <= 1 * ## | | | [8] V4 > 1 * ## | | [9] V9 > 3 ## | | | [10] V8 <= 575 * ## | | | [11] V8 > 575 ## | | | | [12] V7 <= 80 * ## | | | | [13] V7 > 80 * ## ## $nodes[[34]] ## [1] root ## | [2] V2 <= 12 ## | | [3] V8 <= 1175 ## | | | [4] V7 <= 90 ## | | | | [5] V5 <= 1 ## | | | | | [6] V3 <= 66 * ## | | | | | [7] V3 > 66 * ## | | | | [8] V5 > 1 * ## | | | [9] V7 > 90 * ## | | [10] V8 > 1175 * ## | [11] V2 > 12 ## | | [12] V7 <= 60 * ## | | [13] V7 > 60 ## | | | [14] V2 <= 15 * ## | | | [15] V2 > 15 ## | | | | [16] V2 <= 21 * ## | | | | [17] V2 > 21 * ## ## $nodes[[35]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V3 <= 45 * ## | | [4] V3 > 45 ## | | | [5] V9 <= 4 ## | | | | [6] V5 <= 0 * ## | | | | [7] V5 > 0 ## | | | | | [8] V7 <= 80 * ## | | | | | [9] V7 > 80 * ## | | | [10] V9 > 4 ## | | | | [11] V4 <= 1 ## | | | | | [12] V5 <= 0 * ## | | | | | [13] V5 > 0 ## | | | | | | [14] V2 <= 11 * ## | | | | | | [15] V2 > 11 * ## | | | | [16] V4 > 1 * ## | [17] V5 > 1 ## | | [18] V9 <= 10 * ## | | [19] V9 > 10 * ## ## $nodes[[36]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V5 <= 0 ## | | | [4] V9 <= 6 ## | | | | [5] V2 <= 5 * ## | | | | [6] V2 > 5 * ## | | | [7] V9 > 6 * ## | | [8] V5 > 0 ## | | | [9] V4 <= 1 ## | | | | [10] V3 <= 59 * ## | | | | [11] V3 > 59 ## | | | | | [12] V8 <= 825 * ## | | | | | [13] V8 > 825 * ## | | | [14] V4 > 1 * ## | [15] V5 > 1 ## | | [16] V7 <= 60 * ## | | [17] V7 > 60 * ## ## $nodes[[37]] ## [1] root ## | [2] V7 <= 60 ## | | [3] V9 <= 12 * ## | | [4] V9 > 12 * ## | [5] V7 > 60 ## | | [6] V8 <= 1100 ## | | | [7] V4 <= 1 ## | | | | [8] V9 <= 20 ## | | | | | [9] V6 <= 80 * ## | | | | | [10] V6 > 80 * ## | | | | [11] V9 > 20 * ## | | | [12] V4 > 1 ## | | | | [13] V7 <= 80 * ## | | | | [14] V7 > 80 * ## | | [15] V8 > 1100 ## | | | [16] V9 <= 2 * ## | | | [17] V9 > 2 * ## ## $nodes[[38]] ## [1] root ## | [2] V7 <= 60 * ## | [3] V7 > 60 ## | | [4] V5 <= 1 ## | | | [5] V4 <= 1 ## | | | | [6] V5 <= 0 ## | | | | | [7] V9 <= 5 * ## | | | | | [8] V9 > 5 * ## | | | | [9] V5 > 0 ## | | | | | [10] V3 <= 63 * ## | | | | | [11] V3 > 63 * ## | | | [12] V4 > 1 ## | | | | [13] V9 <= 4 * ## | | | | [14] V9 > 4 * ## | | [15] V5 > 1 * ## ## $nodes[[39]] ## [1] root ## | [2] V7 <= 70 ## | | [3] V9 <= 20 ## | | | [4] V4 <= 1 * ## | | | [5] V4 > 1 * ## | | [6] V9 > 20 * ## | [7] V7 > 70 ## | | [8] V5 <= 0 ## | | | [9] V6 <= 90 * ## | | | [10] V6 > 90 * ## | | [11] V5 > 0 ## | | | [12] V2 <= 12 ## | | | | [13] V6 <= 80 ## | | | | | [14] V9 <= 14 * ## | | | | | [15] V9 > 14 * ## | | | | [16] V6 > 80 * ## | | | [17] V2 > 12 * ## ## $nodes[[40]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V7 <= 90 ## | | | [4] V9 <= 4 ## | | | | [5] V4 <= 1 * ## | | | | [6] V4 > 1 * ## | | | [7] V9 > 4 ## | | | | [8] V4 <= 1 ## | | | | | [9] V7 <= 80 ## | | | | | | [10] V7 <= 70 * ## | | | | | | [11] V7 > 70 * ## | | | | | [12] V7 > 80 * ## | | | | [13] V4 > 1 * ## | | [14] V7 > 90 * ## | [15] V5 > 1 ## | | [16] V3 <= 65 * ## | | [17] V3 > 65 * ## ## $nodes[[41]] ## [1] root ## | [2] V3 <= 67 ## | | [3] V6 <= 80 ## | | | [4] V3 <= 56 * ## | | | [5] V3 > 56 ## | | | | [6] V2 <= 15 * ## | | | | [7] V2 > 15 * ## | | [8] V6 > 80 ## | | | [9] V5 <= 0 ## | | | | [10] V3 <= 56 * ## | | | | [11] V3 > 56 * ## | | | [12] V5 > 0 * ## | [13] V3 > 67 ## | | [14] V9 <= 14 ## | | | [15] V6 <= 80 ## | | | | [16] V5 <= 1 * ## | | | | [17] V5 > 1 * ## | | | [18] V6 > 80 * ## | | [19] V9 > 14 * ## ## $nodes[[42]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V3 <= 70 ## | | | [4] V5 <= 1 ## | | | | [5] V7 <= 70 * ## | | | | [6] V7 > 70 ## | | | | | [7] V5 <= 0 * ## | | | | | [8] V5 > 0 * ## | | | [9] V5 > 1 * ## | | [10] V3 > 70 * ## | [11] V4 > 1 ## | | [12] V7 <= 70 * ## | | [13] V7 > 70 ## | | | [14] V2 <= 12 ## | | | | [15] V5 <= 0 * ## | | | | [16] V5 > 0 * ## | | | [17] V2 > 12 * ## ## $nodes[[43]] ## [1] root ## | [2] V5 <= 0 ## | | [3] V6 <= 90 * ## | | [4] V6 > 90 * ## | [5] V5 > 0 ## | | [6] V4 <= 1 ## | | | [7] V2 <= 7 ## | | | | [8] V7 <= 60 * ## | | | | [9] V7 > 60 * ## | | | [10] V2 > 7 ## | | | | [11] V6 <= 70 * ## | | | | [12] V6 > 70 * ## | | [13] V4 > 1 ## | | | [14] V8 <= 675 * ## | | | [15] V8 > 675 * ## ## $nodes[[44]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V5 <= 0 ## | | | [4] V6 <= 90 * ## | | | [5] V6 > 90 * ## | | [6] V5 > 0 ## | | | [7] V4 <= 1 ## | | | | [8] V7 <= 60 * ## | | | | [9] V7 > 60 ## | | | | | [10] V6 <= 80 * ## | | | | | [11] V6 > 80 * ## | | | [12] V4 > 1 * ## | [13] V5 > 1 ## | | [14] V7 <= 60 * ## | | [15] V7 > 60 * ## ## $nodes[[45]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V3 <= 67 ## | | | [4] V6 <= 70 * ## | | | [5] V6 > 70 ## | | | | [6] V5 <= 0 * ## | | | | [7] V5 > 0 ## | | | | | [8] V3 <= 57 * ## | | | | | [9] V3 > 57 * ## | | [10] V3 > 67 ## | | | [11] V9 <= 10 * ## | | | [12] V9 > 10 * ## | [13] V4 > 1 ## | | [14] V5 <= 0 * ## | | [15] V5 > 0 ## | | | [16] V9 <= 0 * ## | | | [17] V9 > 0 * ## ## $nodes[[46]] ## [1] root ## | [2] V5 <= 0 ## | | [3] V7 <= 90 * ## | | [4] V7 > 90 * ## | [5] V5 > 0 ## | | [6] V7 <= 60 * ## | | [7] V7 > 60 ## | | | [8] V2 <= 5 * ## | | | [9] V2 > 5 ## | | | | [10] V3 <= 59 * ## | | | | [11] V3 > 59 ## | | | | | [12] V5 <= 1 ## | | | | | | [13] V6 <= 80 * ## | | | | | | [14] V6 > 80 * ## | | | | | [15] V5 > 1 * ## ## $nodes[[47]] ## [1] root ## | [2] V3 <= 64 ## | | [3] V8 <= 1175 ## | | | [4] V5 <= 0 * ## | | | [5] V5 > 0 ## | | | | [6] V8 <= 925 ## | | | | | [7] V9 <= 14 * ## | | | | | [8] V9 > 14 * ## | | | | [9] V8 > 925 * ## | | [10] V8 > 1175 * ## | [11] V3 > 64 ## | | [12] V9 <= 20 ## | | | [13] V6 <= 70 * ## | | | [14] V6 > 70 ## | | | | [15] V5 <= 0 * ## | | | | [16] V5 > 0 * ## | | [17] V9 > 20 * ## ## $nodes[[48]] ## [1] root ## | [2] V6 <= 70 ## | | [3] V7 <= 60 * ## | | [4] V7 > 60 * ## | [5] V6 > 70 ## | | [6] V4 <= 1 ## | | | [7] V3 <= 65 ## | | | | [8] V7 <= 80 * ## | | | | [9] V7 > 80 * ## | | | [10] V3 > 65 * ## | | [11] V4 > 1 ## | | | [12] V5 <= 0 * ## | | | [13] V5 > 0 * ## ## $nodes[[49]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V7 <= 70 * ## | | [4] V7 > 70 ## | | | [5] V9 <= 12 ## | | | | [6] V6 <= 80 * ## | | | | [7] V6 > 80 ## | | | | | [8] V5 <= 0 ## | | | | | | [9] V3 <= 51 * ## | | | | | | [10] V3 > 51 * ## | | | | | [11] V5 > 0 * ## | | | [12] V9 > 12 * ## | [13] V5 > 1 ## | | [14] V9 <= 20 * ## | | [15] V9 > 20 * ## ## $nodes[[50]] ## [1] root ## | [2] V7 <= 70 ## | | [3] V4 <= 1 ## | | | [4] V9 <= 20 ## | | | | [5] V6 <= 70 * ## | | | | [6] V6 > 70 * ## | | | [7] V9 > 20 * ## | | [8] V4 > 1 * ## | [9] V7 > 70 ## | | [10] V3 <= 63 ## | | | [11] V4 <= 1 ## | | | | [12] V5 <= 0 * ## | | | | [13] V5 > 0 * ## | | | [14] V4 > 1 * ## | | [15] V3 > 63 ## | | | [16] V9 <= 3 * ## | | | [17] V9 > 3 * ## ## $nodes[[51]] ## [1] root ## | [2] V7 <= 70 ## | | [3] V9 <= 20 ## | | | [4] V2 <= 3 * ## | | | [5] V2 > 3 * ## | | [6] V9 > 20 * ## | [7] V7 > 70 ## | | [8] V3 <= 63 ## | | | [9] V4 <= 1 * ## | | | [10] V4 > 1 * ## | | [11] V3 > 63 ## | | | [12] V8 <= 1100 ## | | | | [13] V4 <= 1 * ## | | | | [14] V4 > 1 * ## | | | [15] V8 > 1100 * ## ## $nodes[[52]] ## [1] root ## | [2] V6 <= 70 ## | | [3] V4 <= 1 * ## | | [4] V4 > 1 * ## | [5] V6 > 70 ## | | [6] V5 <= 0 ## | | | [7] V3 <= 63 ## | | | | [8] V8 <= 768 * ## | | | | [9] V8 > 768 * ## | | | [10] V3 > 63 * ## | | [11] V5 > 0 ## | | | [12] V4 <= 1 ## | | | | [13] V8 <= 825 * ## | | | | [14] V8 > 825 * ## | | | [15] V4 > 1 ## | | | | [16] V3 <= 63 * ## | | | | [17] V3 > 63 * ## ## $nodes[[53]] ## [1] root ## | [2] V7 <= 60 * ## | [3] V7 > 60 ## | | [4] V4 <= 1 ## | | | [5] V5 <= 1 ## | | | | [6] V3 <= 65 ## | | | | | [7] V5 <= 0 * ## | | | | | [8] V5 > 0 ## | | | | | | [9] V3 <= 53 * ## | | | | | | [10] V3 > 53 * ## | | | | [11] V3 > 65 * ## | | | [12] V5 > 1 * ## | | [13] V4 > 1 ## | | | [14] V7 <= 80 * ## | | | [15] V7 > 80 * ## ## $nodes[[54]] ## [1] root ## | [2] V3 <= 45 * ## | [3] V3 > 45 ## | | [4] V7 <= 70 ## | | | [5] V2 <= 3 * ## | | | [6] V2 > 3 ## | | | | [7] V9 <= 13 ## | | | | | [8] V8 <= 1025 * ## | | | | | [9] V8 > 1025 * ## | | | | [10] V9 > 13 * ## | | [11] V7 > 70 ## | | | [12] V5 <= 0 * ## | | | [13] V5 > 0 ## | | | | [14] V4 <= 1 ## | | | | | [15] V7 <= 90 * ## | | | | | [16] V7 > 90 * ## | | | | [17] V4 > 1 * ## ## $nodes[[55]] ## [1] root ## | [2] V7 <= 60 * ## | [3] V7 > 60 ## | | [4] V7 <= 80 ## | | | [5] V8 <= 538 * ## | | | [6] V8 > 538 ## | | | | [7] V6 <= 80 * ## | | | | [8] V6 > 80 * ## | | [9] V7 > 80 ## | | | [10] V2 <= 10 ## | | | | [11] V7 <= 90 * ## | | | | [12] V7 > 90 * ## | | | [13] V2 > 10 ## | | | | [14] V4 <= 1 * ## | | | | [15] V4 > 1 * ## ## $nodes[[56]] ## [1] root ## | [2] V5 <= 0 ## | | [3] V7 <= 80 * ## | | [4] V7 > 80 * ## | [5] V5 > 0 ## | | [6] V2 <= 10 ## | | | [7] V5 <= 1 * ## | | | [8] V5 > 1 * ## | | [9] V2 > 10 ## | | | [10] V9 <= 20 ## | | | | [11] V7 <= 80 * ## | | | | [12] V7 > 80 * ## | | | [13] V9 > 20 * ## ## $nodes[[57]] ## [1] root ## | [2] V3 <= 48 * ## | [3] V3 > 48 ## | | [4] V7 <= 80 ## | | | [5] V5 <= 0 * ## | | | [6] V5 > 0 ## | | | | [7] V3 <= 63 * ## | | | | [8] V3 > 63 ## | | | | | [9] V9 <= 20 * ## | | | | | [10] V9 > 20 * ## | | [11] V7 > 80 ## | | | [12] V5 <= 0 * ## | | | [13] V5 > 0 ## | | | | [14] V7 <= 90 * ## | | | | [15] V7 > 90 * ## ## $nodes[[58]] ## [1] root ## | [2] V3 <= 44 * ## | [3] V3 > 44 ## | | [4] V4 <= 1 ## | | | [5] V7 <= 60 * ## | | | [6] V7 > 60 ## | | | | [7] V2 <= 11 ## | | | | | [8] V3 <= 64 * ## | | | | | [9] V3 > 64 * ## | | | | [10] V2 > 11 ## | | | | | [11] V9 <= 5 * ## | | | | | [12] V9 > 5 * ## | | [13] V4 > 1 ## | | | [14] V5 <= 1 ## | | | | [15] V2 <= 12 * ## | | | | [16] V2 > 12 * ## | | | [17] V5 > 1 * ## ## $nodes[[59]] ## [1] root ## | [2] V8 <= 488 * ## | [3] V8 > 488 ## | | [4] V5 <= 0 ## | | | [5] V4 <= 1 * ## | | | [6] V4 > 1 * ## | | [7] V5 > 0 ## | | | [8] V9 <= 20 ## | | | | [9] V8 <= 1100 ## | | | | | [10] V5 <= 1 ## | | | | | | [11] V2 <= 12 * ## | | | | | | [12] V2 > 12 * ## | | | | | [13] V5 > 1 * ## | | | | [14] V8 > 1100 * ## | | | [15] V9 > 20 * ## ## $nodes[[60]] ## [1] root ## | [2] V6 <= 80 ## | | [3] V9 <= 20 ## | | | [4] V5 <= 1 ## | | | | [5] V7 <= 80 * ## | | | | [6] V7 > 80 * ## | | | [7] V5 > 1 * ## | | [8] V9 > 20 * ## | [9] V6 > 80 ## | | [10] V4 <= 1 ## | | | [11] V2 <= 13 ## | | | | [12] V9 <= 5 * ## | | | | [13] V9 > 5 * ## | | | [14] V2 > 13 * ## | | [15] V4 > 1 * ## ## $nodes[[61]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V4 <= 1 ## | | | [4] V7 <= 70 * ## | | | [5] V7 > 70 ## | | | | [6] V8 <= 1039 * ## | | | | [7] V8 > 1039 * ## | | [8] V4 > 1 ## | | | [9] V6 <= 80 * ## | | | [10] V6 > 80 ## | | | | [11] V9 <= 2 * ## | | | | [12] V9 > 2 * ## | [13] V5 > 1 ## | | [14] V9 <= 10 * ## | | [15] V9 > 10 * ## ## $nodes[[62]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V3 <= 63 ## | | | [4] V8 <= 1025 ## | | | | [5] V4 <= 1 * ## | | | | [6] V4 > 1 * ## | | | [7] V8 > 1025 ## | | | | [8] V2 <= 5 * ## | | | | [9] V2 > 5 * ## | | [10] V3 > 63 ## | | | [11] V6 <= 80 * ## | | | [12] V6 > 80 * ## | [13] V5 > 1 ## | | [14] V2 <= 5 * ## | | [15] V2 > 5 * ## ## $nodes[[63]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V8 <= 910 ## | | | [4] V9 <= 20 * ## | | | [5] V9 > 20 * ## | | [6] V8 > 910 ## | | | [7] V8 <= 1225 ## | | | | [8] V5 <= 0 * ## | | | | [9] V5 > 0 * ## | | | [10] V8 > 1225 * ## | [11] V4 > 1 ## | | [12] V8 <= 825 ## | | | [13] V6 <= 80 * ## | | | [14] V6 > 80 * ## | | [15] V8 > 825 ## | | | [16] V5 <= 0 * ## | | | [17] V5 > 0 * ## ## $nodes[[64]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V3 <= 65 ## | | | [4] V5 <= 0 * ## | | | [5] V5 > 0 * ## | | [6] V3 > 65 ## | | | [7] V8 <= 925 * ## | | | [8] V8 > 925 * ## | [9] V4 > 1 ## | | [10] V5 <= 1 ## | | | [11] V7 <= 80 * ## | | | [12] V7 > 80 * ## | | [13] V5 > 1 * ## ## $nodes[[65]] ## [1] root ## | [2] V5 <= 0 ## | | [3] V6 <= 90 * ## | | [4] V6 > 90 * ## | [5] V5 > 0 ## | | [6] V5 <= 1 ## | | | [7] V4 <= 1 ## | | | | [8] V9 <= 2 * ## | | | | [9] V9 > 2 * ## | | | [10] V4 > 1 ## | | | | [11] V8 <= 825 * ## | | | | [12] V8 > 825 * ## | | [13] V5 > 1 ## | | | [14] V8 <= 925 * ## | | | [15] V8 > 925 * ## ## $nodes[[66]] ## [1] root ## | [2] V7 <= 60 * ## | [3] V7 > 60 ## | | [4] V4 <= 1 ## | | | [5] V8 <= 730 * ## | | | [6] V8 > 730 ## | | | | [7] V6 <= 80 * ## | | | | [8] V6 > 80 ## | | | | | [9] V2 <= 13 * ## | | | | | [10] V2 > 13 * ## | | [11] V4 > 1 ## | | | [12] V3 <= 54 * ## | | | [13] V3 > 54 ## | | | | [14] V2 <= 10 * ## | | | | [15] V2 > 10 * ## ## $nodes[[67]] ## [1] root ## | [2] V3 <= 71 ## | | [3] V6 <= 70 * ## | | [4] V6 > 70 ## | | | [5] V4 <= 1 ## | | | | [6] V6 <= 80 * ## | | | | [7] V6 > 80 ## | | | | | [8] V5 <= 0 * ## | | | | | [9] V5 > 0 * ## | | | [10] V4 > 1 ## | | | | [11] V2 <= 12 * ## | | | | [12] V2 > 12 * ## | [13] V3 > 71 * ## ## $nodes[[68]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V6 <= 80 ## | | | [4] V3 <= 69 ## | | | | [5] V2 <= 13 * ## | | | | [6] V2 > 13 * ## | | | [7] V3 > 69 * ## | | [8] V6 > 80 ## | | | [9] V2 <= 13 ## | | | | [10] V2 <= 6 * ## | | | | [11] V2 > 6 * ## | | | [12] V2 > 13 * ## | [13] V4 > 1 ## | | [14] V8 <= 1060 ## | | | [15] V6 <= 80 * ## | | | [16] V6 > 80 * ## | | [17] V8 > 1060 * ## ## $nodes[[69]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V7 <= 60 * ## | | [4] V7 > 60 ## | | | [5] V2 <= 6 * ## | | | [6] V2 > 6 ## | | | | [7] V3 <= 63 * ## | | | | [8] V3 > 63 * ## | [9] V4 > 1 ## | | [10] V7 <= 90 ## | | | [11] V9 <= 0 * ## | | | [12] V9 > 0 ## | | | | [13] V5 <= 1 * ## | | | | [14] V5 > 1 * ## | | [15] V7 > 90 * ## ## $nodes[[70]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V3 <= 71 ## | | | [4] V9 <= 20 ## | | | | [5] V6 <= 90 ## | | | | | [6] V8 <= 1125 ## | | | | | | [7] V9 <= 7 * ## | | | | | | [8] V9 > 7 * ## | | | | | [9] V8 > 1125 * ## | | | | [10] V6 > 90 * ## | | | [11] V9 > 20 * ## | | [12] V3 > 71 * ## | [13] V4 > 1 ## | | [14] V5 <= 0 * ## | | [15] V5 > 0 ## | | | [16] V2 <= 12 * ## | | | [17] V2 > 12 * ## ## $nodes[[71]] ## [1] root ## | [2] V5 <= 0 ## | | [3] V3 <= 64 ## | | | [4] V7 <= 90 * ## | | | [5] V7 > 90 * ## | | [6] V3 > 64 * ## | [7] V5 > 0 ## | | [8] V7 <= 60 * ## | | [9] V7 > 60 ## | | | [10] V4 <= 1 ## | | | | [11] V9 <= 20 ## | | | | | [12] V8 <= 1025 * ## | | | | | [13] V8 > 1025 * ## | | | | [14] V9 > 20 * ## | | | [15] V4 > 1 ## | | | | [16] V2 <= 12 * ## | | | | [17] V2 > 12 * ## ## $nodes[[72]] ## [1] root ## | [2] V7 <= 70 ## | | [3] V2 <= 3 * ## | | [4] V2 > 3 ## | | | [5] V4 <= 1 * ## | | | [6] V4 > 1 * ## | [7] V7 > 70 ## | | [8] V5 <= 0 ## | | | [9] V7 <= 90 * ## | | | [10] V7 > 90 * ## | | [11] V5 > 0 ## | | | [12] V4 <= 1 ## | | | | [13] V3 <= 59 * ## | | | | [14] V3 > 59 * ## | | | [15] V4 > 1 * ## ## $nodes[[73]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V4 <= 1 ## | | | [4] V6 <= 80 ## | | | | [5] V7 <= 80 * ## | | | | [6] V7 > 80 * ## | | | [7] V6 > 80 ## | | | | [8] V3 <= 65 * ## | | | | [9] V3 > 65 * ## | | [10] V4 > 1 ## | | | [11] V6 <= 80 * ## | | | [12] V6 > 80 * ## | [13] V5 > 1 ## | | [14] V9 <= 11 * ## | | [15] V9 > 11 * ## ## $nodes[[74]] ## [1] root ## | [2] V6 <= 80 ## | | [3] V4 <= 1 ## | | | [4] V9 <= 20 ## | | | | [5] V9 <= 8 * ## | | | | [6] V9 > 8 * ## | | | [7] V9 > 20 * ## | | [8] V4 > 1 ## | | | [9] V6 <= 60 * ## | | | [10] V6 > 60 * ## | [11] V6 > 80 ## | | [12] V4 <= 1 ## | | | [13] V9 <= 5 * ## | | | [14] V9 > 5 * ## | | [15] V4 > 1 * ## ## $nodes[[75]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V4 <= 1 ## | | | [4] V5 <= 0 * ## | | | [5] V5 > 0 ## | | | | [6] V9 <= 14 ## | | | | | [7] V7 <= 80 * ## | | | | | [8] V7 > 80 * ## | | | | [9] V9 > 14 * ## | | [10] V4 > 1 ## | | | [11] V9 <= 10 * ## | | | [12] V9 > 10 * ## | [13] V5 > 1 ## | | [14] V2 <= 13 * ## | | [15] V2 > 13 * ## ## $nodes[[76]] ## [1] root ## | [2] V7 <= 60 * ## | [3] V7 > 60 ## | | [4] V3 <= 64 ## | | | [5] V6 <= 80 * ## | | | [6] V6 > 80 ## | | | | [7] V2 <= 13 * ## | | | | [8] V2 > 13 * ## | | [9] V3 > 64 ## | | | [10] V8 <= 575 * ## | | | [11] V8 > 575 ## | | | | [12] V8 <= 910 * ## | | | | [13] V8 > 910 ## | | | | | [14] V8 <= 1100 * ## | | | | | [15] V8 > 1100 * ## ## $nodes[[77]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V7 <= 60 * ## | | [4] V7 > 60 ## | | | [5] V5 <= 1 ## | | | | [6] V5 <= 0 * ## | | | | [7] V5 > 0 ## | | | | | [8] V8 <= 825 * ## | | | | | [9] V8 > 825 ## | | | | | | [10] V7 <= 80 * ## | | | | | | [11] V7 > 80 * ## | | | [12] V5 > 1 * ## | [13] V4 > 1 ## | | [14] V9 <= -1 * ## | | [15] V9 > -1 ## | | | [16] V7 <= 80 * ## | | | [17] V7 > 80 * ## ## $nodes[[78]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V7 <= 60 * ## | | [4] V7 > 60 ## | | | [5] V3 <= 68 ## | | | | [6] V6 <= 80 * ## | | | | [7] V6 > 80 ## | | | | | [8] V7 <= 80 * ## | | | | | [9] V7 > 80 * ## | | | [10] V3 > 68 * ## | [11] V4 > 1 ## | | [12] V9 <= -1 * ## | | [13] V9 > -1 ## | | | [14] V6 <= 70 * ## | | | [15] V6 > 70 ## | | | | [16] V8 <= 925 * ## | | | | [17] V8 > 925 * ## ## $nodes[[79]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V5 <= 0 ## | | | [4] V8 <= 463 * ## | | | [5] V8 > 463 ## | | | | [6] V7 <= 80 * ## | | | | [7] V7 > 80 * ## | | [8] V5 > 0 ## | | | [9] V6 <= 80 ## | | | | [10] V9 <= 0 * ## | | | | [11] V9 > 0 ## | | | | | [12] V3 <= 66 * ## | | | | | [13] V3 > 66 * ## | | | [14] V6 > 80 ## | | | | [15] V4 <= 1 * ## | | | | [16] V4 > 1 * ## | [17] V5 > 1 ## | | [18] V4 <= 1 * ## | | [19] V4 > 1 * ## ## $nodes[[80]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V7 <= 90 ## | | | [4] V6 <= 80 ## | | | | [5] V2 <= 11 * ## | | | | [6] V2 > 11 * ## | | | [7] V6 > 80 ## | | | | [8] V7 <= 80 * ## | | | | [9] V7 > 80 * ## | | [10] V7 > 90 * ## | [11] V5 > 1 ## | | [12] V2 <= 13 * ## | | [13] V2 > 13 * ## ## $nodes[[81]] ## [1] root ## | [2] V5 <= 0 ## | | [3] V2 <= 11 * ## | | [4] V2 > 11 * ## | [5] V5 > 0 ## | | [6] V4 <= 1 ## | | | [7] V9 <= 20 ## | | | | [8] V6 <= 70 * ## | | | | [9] V6 > 70 ## | | | | | [10] V9 <= 8 * ## | | | | | [11] V9 > 8 * ## | | | [12] V9 > 20 * ## | | [13] V4 > 1 ## | | | [14] V9 <= 10 * ## | | | [15] V9 > 10 * ## ## $nodes[[82]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V4 <= 1 ## | | | [4] V3 <= 65 ## | | | | [5] V9 <= 1 * ## | | | | [6] V9 > 1 ## | | | | | [7] V2 <= 4 * ## | | | | | [8] V2 > 4 * ## | | | [9] V3 > 65 ## | | | | [10] V6 <= 80 * ## | | | | [11] V6 > 80 * ## | | [12] V4 > 1 ## | | | [13] V7 <= 80 * ## | | | [14] V7 > 80 * ## | [15] V5 > 1 ## | | [16] V9 <= 20 * ## | | [17] V9 > 20 * ## ## $nodes[[83]] ## [1] root ## | [2] V3 <= 70 ## | | [3] V8 <= 925 ## | | | [4] V5 <= 0 * ## | | | [5] V5 > 0 ## | | | | [6] V2 <= 6 * ## | | | | [7] V2 > 6 * ## | | [8] V8 > 925 ## | | | [9] V4 <= 1 ## | | | | [10] V8 <= 1025 * ## | | | | [11] V8 > 1025 * ## | | | [12] V4 > 1 * ## | [13] V3 > 70 * ## ## $nodes[[84]] ## [1] root ## | [2] V5 <= 0 ## | | [3] V2 <= 12 ## | | | [4] V9 <= 3 * ## | | | [5] V9 > 3 * ## | | [6] V2 > 12 * ## | [7] V5 > 0 ## | | [8] V3 <= 50 * ## | | [9] V3 > 50 ## | | | [10] V5 <= 1 ## | | | | [11] V2 <= 13 ## | | | | | [12] V9 <= 14 * ## | | | | | [13] V9 > 14 * ## | | | | [14] V2 > 13 * ## | | | [15] V5 > 1 ## | | | | [16] V2 <= 13 * ## | | | | [17] V2 > 13 * ## ## $nodes[[85]] ## [1] root ## | [2] V7 <= 60 ## | | [3] V9 <= 13 * ## | | [4] V9 > 13 * ## | [5] V7 > 60 ## | | [6] V6 <= 70 * ## | | [7] V6 > 70 ## | | | [8] V2 <= 13 ## | | | | [9] V5 <= 0 * ## | | | | [10] V5 > 0 ## | | | | | [11] V9 <= 13 * ## | | | | | [12] V9 > 13 * ## | | | [13] V2 > 13 * ## ## $nodes[[86]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V9 <= 2 * ## | | [4] V9 > 2 ## | | | [5] V3 <= 70 ## | | | | [6] V6 <= 80 ## | | | | | [7] V3 <= 62 * ## | | | | | [8] V3 > 62 * ## | | | | [9] V6 > 80 * ## | | | [10] V3 > 70 * ## | [11] V4 > 1 ## | | [12] V7 <= 90 ## | | | [13] V2 <= 12 ## | | | | [14] V2 <= 3 * ## | | | | [15] V2 > 3 * ## | | | [16] V2 > 12 * ## | | [17] V7 > 90 * ## ## $nodes[[87]] ## [1] root ## | [2] V7 <= 60 ## | | [3] V9 <= 14 * ## | | [4] V9 > 14 * ## | [5] V7 > 60 ## | | [6] V5 <= 1 ## | | | [7] V9 <= 5 ## | | | | [8] V6 <= 80 * ## | | | | [9] V6 > 80 ## | | | | | [10] V7 <= 80 * ## | | | | | [11] V7 > 80 ## | | | | | | [12] V5 <= 0 * ## | | | | | | [13] V5 > 0 * ## | | | [14] V9 > 5 ## | | | | [15] V4 <= 1 * ## | | | | [16] V4 > 1 * ## | | [17] V5 > 1 * ## ## $nodes[[88]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V8 <= 875 ## | | | [4] V3 <= 64 * ## | | | [5] V3 > 64 * ## | | [6] V8 > 875 ## | | | [7] V7 <= 70 * ## | | | [8] V7 > 70 ## | | | | [9] V5 <= 0 * ## | | | | [10] V5 > 0 * ## | [11] V4 > 1 ## | | [12] V6 <= 70 * ## | | [13] V6 > 70 ## | | | [14] V9 <= 3 * ## | | | [15] V9 > 3 * ## ## $nodes[[89]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V6 <= 70 * ## | | [4] V6 > 70 ## | | | [5] V2 <= 16 ## | | | | [6] V8 <= 575 * ## | | | | [7] V8 > 575 ## | | | | | [8] V5 <= 0 * ## | | | | | [9] V5 > 0 * ## | | | [10] V2 > 16 * ## | [11] V4 > 1 ## | | [12] V6 <= 70 * ## | | [13] V6 > 70 ## | | | [14] V9 <= 14 ## | | | | [15] V2 <= 5 * ## | | | | [16] V2 > 5 * ## | | | [17] V9 > 14 * ## ## $nodes[[90]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V5 <= 0 * ## | | [4] V5 > 0 ## | | | [5] V8 <= 1275 ## | | | | [6] V5 <= 1 ## | | | | | [7] V6 <= 80 * ## | | | | | [8] V6 > 80 * ## | | | | [9] V5 > 1 * ## | | | [10] V8 > 1275 * ## | [11] V4 > 1 ## | | [12] V6 <= 70 * ## | | [13] V6 > 70 ## | | | [14] V7 <= 80 * ## | | | [15] V7 > 80 * ## ## $nodes[[91]] ## [1] root ## | [2] V7 <= 70 ## | | [3] V7 <= 60 * ## | | [4] V7 > 60 * ## | [5] V7 > 70 ## | | [6] V4 <= 1 ## | | | [7] V5 <= 0 * ## | | | [8] V5 > 0 ## | | | | [9] V7 <= 80 * ## | | | | [10] V7 > 80 * ## | | [11] V4 > 1 ## | | | [12] V7 <= 80 * ## | | | [13] V7 > 80 * ## ## $nodes[[92]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V7 <= 70 * ## | | [4] V7 > 70 ## | | | [5] V4 <= 1 ## | | | | [6] V9 <= 5 * ## | | | | [7] V9 > 5 * ## | | | [8] V4 > 1 ## | | | | [9] V2 <= 12 ## | | | | | [10] V3 <= 64 * ## | | | | | [11] V3 > 64 * ## | | | | [12] V2 > 12 * ## | [13] V5 > 1 ## | | [14] V9 <= 11 * ## | | [15] V9 > 11 * ## ## $nodes[[93]] ## [1] root ## | [2] V3 <= 51 * ## | [3] V3 > 51 ## | | [4] V4 <= 1 ## | | | [5] V7 <= 80 ## | | | | [6] V3 <= 65 * ## | | | | [7] V3 > 65 ## | | | | | [8] V9 <= 17 * ## | | | | | [9] V9 > 17 * ## | | | [10] V7 > 80 ## | | | | [11] V8 <= 993 * ## | | | | [12] V8 > 993 * ## | | [13] V4 > 1 ## | | | [14] V5 <= 0 * ## | | | [15] V5 > 0 ## | | | | [16] V3 <= 60 * ## | | | | [17] V3 > 60 * ## ## $nodes[[94]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V9 <= 12 ## | | | [4] V5 <= 0 ## | | | | [5] V4 <= 1 * ## | | | | [6] V4 > 1 * ## | | | [7] V5 > 0 ## | | | | [8] V4 <= 1 * ## | | | | [9] V4 > 1 * ## | | [10] V9 > 12 ## | | | [11] V4 <= 1 * ## | | | [12] V4 > 1 * ## | [13] V5 > 1 ## | | [14] V7 <= 60 * ## | | [15] V7 > 60 * ## ## $nodes[[95]] ## [1] root ## | [2] V3 <= 46 * ## | [3] V3 > 46 ## | | [4] V7 <= 60 ## | | | [5] V9 <= 13 * ## | | | [6] V9 > 13 * ## | | [7] V7 > 60 ## | | | [8] V6 <= 70 * ## | | | [9] V6 > 70 ## | | | | [10] V3 <= 63 ## | | | | | [11] V5 <= 0 * ## | | | | | [12] V5 > 0 * ## | | | | [13] V3 > 63 ## | | | | | [14] V8 <= 993 * ## | | | | | [15] V8 > 993 * ## ## $nodes[[96]] ## [1] root ## | [2] V7 <= 60 * ## | [3] V7 > 60 ## | | [4] V3 <= 64 ## | | | [5] V9 <= 3 ## | | | | [6] V9 <= -1 * ## | | | | [7] V9 > -1 * ## | | | [8] V9 > 3 ## | | | | [9] V5 <= 0 * ## | | | | [10] V5 > 0 * ## | | [11] V3 > 64 ## | | | [12] V5 <= 1 ## | | | | [13] V3 <= 68 * ## | | | | [14] V3 > 68 * ## | | | [15] V5 > 1 * ## ## $nodes[[97]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V5 <= 0 * ## | | [4] V5 > 0 ## | | | [5] V9 <= 24 ## | | | | [6] V6 <= 70 * ## | | | | [7] V6 > 70 * ## | | | [8] V9 > 24 * ## | [9] V4 > 1 ## | | [10] V5 <= 1 ## | | | [11] V9 <= 2 * ## | | | [12] V9 > 2 * ## | | [13] V5 > 1 * ## ## $nodes[[98]] ## [1] root ## | [2] V7 <= 90 ## | | [3] V5 <= 1 ## | | | [4] V9 <= 2 ## | | | | [5] V5 <= 0 * ## | | | | [6] V5 > 0 * ## | | | [7] V9 > 2 ## | | | | [8] V8 <= 1175 ## | | | | | [9] V6 <= 80 * ## | | | | | [10] V6 > 80 * ## | | | | [11] V8 > 1175 * ## | | [12] V5 > 1 ## | | | [13] V9 <= 20 * ## | | | [14] V9 > 20 * ## | [15] V7 > 90 * ## ## $nodes[[99]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V5 <= 0 * ## | | [4] V5 > 0 ## | | | [5] V8 <= 925 ## | | | | [6] V8 <= 513 * ## | | | | [7] V8 > 513 * ## | | | [8] V8 > 925 ## | | | | [9] V3 <= 68 ## | | | | | [10] V2 <= 12 * ## | | | | | [11] V2 > 12 * ## | | | | [12] V3 > 68 * ## | [13] V4 > 1 ## | | [14] V6 <= 80 ## | | | [15] V2 <= 13 * ## | | | [16] V2 > 13 * ## | | [17] V6 > 80 * ## ## $nodes[[100]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V7 <= 60 * ## | | [4] V7 > 60 ## | | | [5] V5 <= 0 * ## | | | [6] V5 > 0 ## | | | | [7] V8 <= 993 * ## | | | | [8] V8 > 993 ## | | | | | [9] V5 <= 1 * ## | | | | | [10] V5 > 1 * ## | [11] V4 > 1 ## | | [12] V9 <= 14 ## | | | [13] V6 <= 80 * ## | | | [14] V6 > 80 * ## | | [15] V9 > 14 * ## ## $nodes[[101]] ## [1] root ## | [2] V8 <= 488 ## | | [3] V9 <= 20 * ## | | [4] V9 > 20 * ## | [5] V8 > 488 ## | | [6] V5 <= 0 ## | | | [7] V4 <= 1 * ## | | | [8] V4 > 1 * ## | | [9] V5 > 0 ## | | | [10] V7 <= 80 ## | | | | [11] V6 <= 80 ## | | | | | [12] V3 <= 56 * ## | | | | | [13] V3 > 56 ## | | | | | | [14] V3 <= 65 * ## | | | | | | [15] V3 > 65 * ## | | | | [16] V6 > 80 * ## | | | [17] V7 > 80 ## | | | | [18] V8 <= 910 * ## | | | | [19] V8 > 910 * ## ## $nodes[[102]] ## [1] root ## | [2] V6 <= 70 ## | | [3] V2 <= 13 * ## | | [4] V2 > 13 * ## | [5] V6 > 70 ## | | [6] V7 <= 90 ## | | | [7] V4 <= 1 ## | | | | [8] V3 <= 59 * ## | | | | [9] V3 > 59 ## | | | | | [10] V5 <= 0 * ## | | | | | [11] V5 > 0 * ## | | | [12] V4 > 1 ## | | | | [13] V3 <= 62 * ## | | | | [14] V3 > 62 * ## | | [15] V7 > 90 * ## ## $nodes[[103]] ## [1] root ## | [2] V6 <= 70 ## | | [3] V7 <= 60 * ## | | [4] V7 > 60 * ## | [5] V6 > 70 ## | | [6] V4 <= 1 ## | | | [7] V3 <= 60 * ## | | | [8] V3 > 60 ## | | | | [9] V7 <= 70 * ## | | | | [10] V7 > 70 * ## | | [11] V4 > 1 ## | | | [12] V6 <= 80 * ## | | | [13] V6 > 80 * ## ## $nodes[[104]] ## [1] root ## | [2] V5 <= 0 ## | | [3] V8 <= 463 * ## | | [4] V8 > 463 ## | | | [5] V4 <= 1 * ## | | | [6] V4 > 1 * ## | [7] V5 > 0 ## | | [8] V4 <= 1 ## | | | [9] V3 <= 71 ## | | | | [10] V2 <= 15 ## | | | | | [11] V9 <= 17 ## | | | | | | [12] V9 <= 1 * ## | | | | | | [13] V9 > 1 * ## | | | | | [14] V9 > 17 * ## | | | | [15] V2 > 15 * ## | | | [16] V3 > 71 * ## | | [17] V4 > 1 ## | | | [18] V5 <= 1 * ## | | | [19] V5 > 1 * ## ## $nodes[[105]] ## [1] root ## | [2] V6 <= 70 ## | | [3] V4 <= 1 * ## | | [4] V4 > 1 * ## | [5] V6 > 70 ## | | [6] V5 <= 0 ## | | | [7] V2 <= 13 ## | | | | [8] V8 <= 725 * ## | | | | [9] V8 > 725 * ## | | | [10] V2 > 13 * ## | | [11] V5 > 0 ## | | | [12] V8 <= 588 * ## | | | [13] V8 > 588 ## | | | | [14] V7 <= 70 * ## | | | | [15] V7 > 70 ## | | | | | [16] V3 <= 64 * ## | | | | | [17] V3 > 64 * ## ## $nodes[[106]] ## [1] root ## | [2] V2 <= 12 ## | | [3] V6 <= 90 ## | | | [4] V2 <= 2 * ## | | | [5] V2 > 2 ## | | | | [6] V3 <= 59 * ## | | | | [7] V3 > 59 ## | | | | | [8] V4 <= 1 * ## | | | | | [9] V4 > 1 * ## | | [10] V6 > 90 * ## | [11] V2 > 12 ## | | [12] V2 <= 16 * ## | | [13] V2 > 16 * ## ## $nodes[[107]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V5 <= 1 ## | | | [4] V7 <= 70 * ## | | | [5] V7 > 70 ## | | | | [6] V9 <= 8 ## | | | | | [7] V5 <= 0 * ## | | | | | [8] V5 > 0 * ## | | | | [9] V9 > 8 * ## | | [10] V5 > 1 * ## | [11] V4 > 1 ## | | [12] V9 <= -2 * ## | | [13] V9 > -2 ## | | | [14] V6 <= 80 * ## | | | [15] V6 > 80 * ## ## $nodes[[108]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V4 <= 1 ## | | | [4] V9 <= 1 * ## | | | [5] V9 > 1 ## | | | | [6] V2 <= 11 ## | | | | | [7] V7 <= 80 * ## | | | | | [8] V7 > 80 * ## | | | | [9] V2 > 11 * ## | | [10] V4 > 1 ## | | | [11] V8 <= 538 * ## | | | [12] V8 > 538 ## | | | | [13] V9 <= 0 * ## | | | | [14] V9 > 0 * ## | [15] V5 > 1 * ## ## $nodes[[109]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V6 <= 70 * ## | | [4] V6 > 70 ## | | | [5] V9 <= 5 ## | | | | [6] V3 <= 63 * ## | | | | [7] V3 > 63 * ## | | | [8] V9 > 5 ## | | | | [9] V8 <= 1100 ## | | | | | [10] V9 <= 14 * ## | | | | | [11] V9 > 14 * ## | | | | [12] V8 > 1100 * ## | [13] V4 > 1 ## | | [14] V6 <= 80 * ## | | [15] V6 > 80 * ## ## $nodes[[110]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V3 <= 71 ## | | | [4] V5 <= 1 ## | | | | [5] V2 <= 16 ## | | | | | [6] V7 <= 80 ## | | | | | | [7] V9 <= 5 * ## | | | | | | [8] V9 > 5 * ## | | | | | [9] V7 > 80 * ## | | | | [10] V2 > 16 * ## | | | [11] V5 > 1 * ## | | [12] V3 > 71 * ## | [13] V4 > 1 ## | | [14] V7 <= 80 ## | | | [15] V5 <= 1 * ## | | | [16] V5 > 1 * ## | | [17] V7 > 80 * ## ## $nodes[[111]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V6 <= 70 * ## | | [4] V6 > 70 ## | | | [5] V7 <= 90 ## | | | | [6] V9 <= 14 ## | | | | | [7] V8 <= 1175 ## | | | | | | [8] V9 <= 5 * ## | | | | | | [9] V9 > 5 * ## | | | | | [10] V8 > 1175 * ## | | | | [11] V9 > 14 * ## | | | [12] V7 > 90 * ## | [13] V4 > 1 ## | | [14] V9 <= 2 * ## | | [15] V9 > 2 ## | | | [16] V2 <= 12 * ## | | | [17] V2 > 12 * ## ## $nodes[[112]] ## [1] root ## | [2] V5 <= 0 ## | | [3] V9 <= 6 * ## | | [4] V9 > 6 * ## | [5] V5 > 0 ## | | [6] V9 <= 27 ## | | | [7] V5 <= 1 ## | | | | [8] V4 <= 1 ## | | | | | [9] V2 <= 11 * ## | | | | | [10] V2 > 11 * ## | | | | [11] V4 > 1 * ## | | | [12] V5 > 1 * ## | | [13] V9 > 27 * ## ## $nodes[[113]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V9 <= 5 ## | | | [4] V6 <= 80 * ## | | | [5] V6 > 80 ## | | | | [6] V6 <= 90 * ## | | | | [7] V6 > 90 * ## | | [8] V9 > 5 ## | | | [9] V3 <= 64 ## | | | | [10] V7 <= 80 * ## | | | | [11] V7 > 80 * ## | | | [12] V3 > 64 * ## | [13] V5 > 1 ## | | [14] V4 <= 1 * ## | | [15] V4 > 1 * ## ## $nodes[[114]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V7 <= 60 * ## | | [4] V7 > 60 ## | | | [5] V8 <= 925 ## | | | | [6] V3 <= 64 * ## | | | | [7] V3 > 64 * ## | | | [8] V8 > 925 ## | | | | [9] V5 <= 0 * ## | | | | [10] V5 > 0 * ## | [11] V4 > 1 ## | | [12] V7 <= 80 * ## | | [13] V7 > 80 ## | | | [14] V5 <= 0 * ## | | | [15] V5 > 0 * ## ## $nodes[[115]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V3 <= 64 ## | | | [4] V7 <= 90 ## | | | | [5] V5 <= 0 * ## | | | | [6] V5 > 0 ## | | | | | [7] V3 <= 53 * ## | | | | | [8] V3 > 53 * ## | | | [9] V7 > 90 * ## | | [10] V3 > 64 ## | | | [11] V6 <= 80 * ## | | | [12] V6 > 80 * ## | [13] V5 > 1 ## | | [14] V6 <= 60 * ## | | [15] V6 > 60 * ## ## $nodes[[116]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V8 <= 1275 ## | | | [4] V5 <= 1 ## | | | | [5] V9 <= 11 ## | | | | | [6] V5 <= 0 * ## | | | | | [7] V5 > 0 * ## | | | | [8] V9 > 11 * ## | | | [9] V5 > 1 * ## | | [10] V8 > 1275 * ## | [11] V4 > 1 ## | | [12] V2 <= 11 * ## | | [13] V2 > 11 * ## ## $nodes[[117]] ## [1] root ## | [2] V6 <= 80 ## | | [3] V9 <= 27 ## | | | [4] V9 <= 1 * ## | | | [5] V9 > 1 ## | | | | [6] V5 <= 1 * ## | | | | [7] V5 > 1 * ## | | [8] V9 > 27 * ## | [9] V6 > 80 ## | | [10] V3 <= 64 ## | | | [11] V4 <= 1 * ## | | | [12] V4 > 1 * ## | | [13] V3 > 64 * ## ## $nodes[[118]] ## [1] root ## | [2] V3 <= 45 * ## | [3] V3 > 45 ## | | [4] V5 <= 1 ## | | | [5] V2 <= 1 * ## | | | [6] V2 > 1 ## | | | | [7] V7 <= 90 ## | | | | | [8] V3 <= 66 ## | | | | | | [9] V5 <= 0 * ## | | | | | | [10] V5 > 0 * ## | | | | | [11] V3 > 66 * ## | | | | [12] V7 > 90 * ## | | [13] V5 > 1 ## | | | [14] V2 <= 13 * ## | | | [15] V2 > 13 * ## ## $nodes[[119]] ## [1] root ## | [2] V3 <= 64 ## | | [3] V5 <= 0 * ## | | [4] V5 > 0 ## | | | [5] V3 <= 50 * ## | | | [6] V3 > 50 ## | | | | [7] V8 <= 768 * ## | | | | [8] V8 > 768 ## | | | | | [9] V9 <= 5 * ## | | | | | [10] V9 > 5 * ## | [11] V3 > 64 ## | | [12] V2 <= 5 * ## | | [13] V2 > 5 ## | | | [14] V8 <= 875 * ## | | | [15] V8 > 875 ## | | | | [16] V2 <= 15 * ## | | | | [17] V2 > 15 * ## ## $nodes[[120]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V4 <= 1 ## | | | [4] V5 <= 0 * ## | | | [5] V5 > 0 ## | | | | [6] V6 <= 80 * ## | | | | [7] V6 > 80 * ## | | [8] V4 > 1 ## | | | [9] V9 <= 1 * ## | | | [10] V9 > 1 * ## | [11] V5 > 1 ## | | [12] V8 <= 910 * ## | | [13] V8 > 910 * ## ## $nodes[[121]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V4 <= 1 ## | | | [4] V7 <= 90 ## | | | | [5] V5 <= 0 * ## | | | | [6] V5 > 0 ## | | | | | [7] V7 <= 70 * ## | | | | | [8] V7 > 70 * ## | | | [9] V7 > 90 * ## | | [10] V4 > 1 ## | | | [11] V6 <= 80 * ## | | | [12] V6 > 80 * ## | [13] V5 > 1 ## | | [14] V4 <= 1 * ## | | [15] V4 > 1 * ## ## $nodes[[122]] ## [1] root ## | [2] V6 <= 70 ## | | [3] V5 <= 1 * ## | | [4] V5 > 1 * ## | [5] V6 > 70 ## | | [6] V7 <= 70 * ## | | [7] V7 > 70 ## | | | [8] V3 <= 64 ## | | | | [9] V2 <= 12 ## | | | | | [10] V9 <= 1 * ## | | | | | [11] V9 > 1 * ## | | | | [12] V2 > 12 * ## | | | [13] V3 > 64 ## | | | | [14] V8 <= 1030 * ## | | | | [15] V8 > 1030 * ## ## $nodes[[123]] ## [1] root ## | [2] V5 <= 0 ## | | [3] V3 <= 60 * ## | | [4] V3 > 60 * ## | [5] V5 > 0 ## | | [6] V6 <= 70 ## | | | [7] V2 <= 13 * ## | | | [8] V2 > 13 * ## | | [9] V6 > 70 ## | | | [10] V7 <= 90 ## | | | | [11] V3 <= 54 * ## | | | | [12] V3 > 54 ## | | | | | [13] V4 <= 1 * ## | | | | | [14] V4 > 1 * ## | | | [15] V7 > 90 * ## ## $nodes[[124]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V5 <= 0 * ## | | [4] V5 > 0 ## | | | [5] V5 <= 1 ## | | | | [6] V3 <= 63 * ## | | | | [7] V3 > 63 * ## | | | [8] V5 > 1 * ## | [9] V4 > 1 ## | | [10] V6 <= 60 * ## | | [11] V6 > 60 ## | | | [12] V3 <= 64 ## | | | | [13] V3 <= 58 * ## | | | | [14] V3 > 58 * ## | | | [15] V3 > 64 * ## ## $nodes[[125]] ## [1] root ## | [2] V7 <= 70 ## | | [3] V9 <= 24 ## | | | [4] V4 <= 1 ## | | | | [5] V5 <= 1 * ## | | | | [6] V5 > 1 * ## | | | [7] V4 > 1 * ## | | [8] V9 > 24 * ## | [9] V7 > 70 ## | | [10] V4 <= 1 ## | | | [11] V9 <= 6 ## | | | | [12] V5 <= 0 * ## | | | | [13] V5 > 0 * ## | | | [14] V9 > 6 * ## | | [15] V4 > 1 ## | | | [16] V7 <= 90 ## | | | | [17] V2 <= 12 * ## | | | | [18] V2 > 12 * ## | | | [19] V7 > 90 * ## ## $nodes[[126]] ## [1] root ## | [2] V7 <= 70 ## | | [3] V2 <= 3 * ## | | [4] V2 > 3 ## | | | [5] V3 <= 63 * ## | | | [6] V3 > 63 ## | | | | [7] V9 <= 11 * ## | | | | [8] V9 > 11 * ## | [9] V7 > 70 ## | | [10] V7 <= 90 ## | | | [11] V9 <= 6 ## | | | | [12] V6 <= 80 * ## | | | | [13] V6 > 80 * ## | | | [14] V9 > 6 ## | | | | [15] V6 <= 80 * ## | | | | [16] V6 > 80 * ## | | [17] V7 > 90 * ## ## $nodes[[127]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V6 <= 80 ## | | | [4] V7 <= 80 * ## | | | [5] V7 > 80 * ## | | [6] V6 > 80 ## | | | [7] V4 <= 1 ## | | | | [8] V8 <= 875 * ## | | | | [9] V8 > 875 ## | | | | | [10] V2 <= 13 * ## | | | | | [11] V2 > 13 * ## | | | [12] V4 > 1 ## | | | | [13] V3 <= 58 * ## | | | | [14] V3 > 58 * ## | [15] V5 > 1 ## | | [16] V2 <= 12 * ## | | [17] V2 > 12 * ## ## $nodes[[128]] ## [1] root ## | [2] V3 <= 45 * ## | [3] V3 > 45 ## | | [4] V6 <= 80 ## | | | [5] V4 <= 1 ## | | | | [6] V9 <= 20 ## | | | | | [7] V6 <= 70 * ## | | | | | [8] V6 > 70 * ## | | | | [9] V9 > 20 * ## | | | [10] V4 > 1 ## | | | | [11] V6 <= 70 * ## | | | | [12] V6 > 70 * ## | | [13] V6 > 80 ## | | | [14] V7 <= 80 * ## | | | [15] V7 > 80 ## | | | | [16] V9 <= 3 * ## | | | | [17] V9 > 3 * ## ## $nodes[[129]] ## [1] root ## | [2] V5 <= 0 ## | | [3] V4 <= 1 * ## | | [4] V4 > 1 * ## | [5] V5 > 0 ## | | [6] V4 <= 1 ## | | | [7] V5 <= 1 ## | | | | [8] V7 <= 80 ## | | | | | [9] V7 <= 70 * ## | | | | | [10] V7 > 70 * ## | | | | [11] V7 > 80 * ## | | | [12] V5 > 1 * ## | | [13] V4 > 1 ## | | | [14] V2 <= 12 * ## | | | [15] V2 > 12 * ## ## $nodes[[130]] ## [1] root ## | [2] V3 <= 47 * ## | [3] V3 > 47 ## | | [4] V4 <= 1 ## | | | [5] V7 <= 60 * ## | | | [6] V7 > 60 ## | | | | [7] V2 <= 16 ## | | | | | [8] V8 <= 875 * ## | | | | | [9] V8 > 875 ## | | | | | | [10] V7 <= 80 * ## | | | | | | [11] V7 > 80 * ## | | | | [12] V2 > 16 * ## | | [13] V4 > 1 ## | | | [14] V6 <= 90 ## | | | | [15] V7 <= 80 * ## | | | | [16] V7 > 80 * ## | | | [17] V6 > 90 * ## ## $nodes[[131]] ## [1] root ## | [2] V7 <= 60 * ## | [3] V7 > 60 ## | | [4] V3 <= 70 ## | | | [5] V7 <= 90 ## | | | | [6] V5 <= 0 ## | | | | | [7] V8 <= 1039 * ## | | | | | [8] V8 > 1039 * ## | | | | [9] V5 > 0 ## | | | | | [10] V9 <= 12 * ## | | | | | [11] V9 > 12 * ## | | | [12] V7 > 90 * ## | | [13] V3 > 70 * ## ## $nodes[[132]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V6 <= 70 * ## | | [4] V6 > 70 ## | | | [5] V2 <= 4 * ## | | | [6] V2 > 4 ## | | | | [7] V3 <= 60 * ## | | | | [8] V3 > 60 ## | | | | | [9] V8 <= 1030 ## | | | | | | [10] V6 <= 80 * ## | | | | | | [11] V6 > 80 * ## | | | | | [12] V8 > 1030 * ## | [13] V4 > 1 ## | | [14] V5 <= 1 ## | | | [15] V3 <= 60 ## | | | | [16] V6 <= 80 * ## | | | | [17] V6 > 80 * ## | | | [18] V3 > 60 * ## | | [19] V5 > 1 * ## ## $nodes[[133]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V4 <= 1 ## | | | [4] V8 <= 1125 ## | | | | [5] V9 <= 14 ## | | | | | [6] V7 <= 70 * ## | | | | | [7] V7 > 70 * ## | | | | [8] V9 > 14 * ## | | | [9] V8 > 1125 * ## | | [10] V4 > 1 ## | | | [11] V7 <= 90 ## | | | | [12] V2 <= 12 ## | | | | | [13] V8 <= 925 * ## | | | | | [14] V8 > 925 * ## | | | | [15] V2 > 12 * ## | | | [16] V7 > 90 * ## | [17] V5 > 1 * ## ## $nodes[[134]] ## [1] root ## | [2] V6 <= 70 ## | | [3] V9 <= 20 ## | | | [4] V4 <= 1 * ## | | | [5] V4 > 1 * ## | | [6] V9 > 20 * ## | [7] V6 > 70 ## | | [8] V7 <= 70 * ## | | [9] V7 > 70 ## | | | [10] V5 <= 0 ## | | | | [11] V7 <= 90 * ## | | | | [12] V7 > 90 * ## | | | [13] V5 > 0 ## | | | | [14] V7 <= 90 ## | | | | | [15] V2 <= 12 * ## | | | | | [16] V2 > 12 * ## | | | | [17] V7 > 90 * ## ## $nodes[[135]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V4 <= 1 ## | | | [4] V3 <= 65 ## | | | | [5] V2 <= 4 * ## | | | | [6] V2 > 4 ## | | | | | [7] V8 <= 1025 * ## | | | | | [8] V8 > 1025 * ## | | | [9] V3 > 65 * ## | | [10] V4 > 1 ## | | | [11] V7 <= 80 * ## | | | [12] V7 > 80 * ## | [13] V5 > 1 ## | | [14] V2 <= 13 ## | | | [15] V8 <= 910 * ## | | | [16] V8 > 910 * ## | | [17] V2 > 13 * ## ## $nodes[[136]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V4 <= 1 ## | | | [4] V3 <= 65 ## | | | | [5] V5 <= 0 * ## | | | | [6] V5 > 0 * ## | | | [7] V3 > 65 ## | | | | [8] V9 <= 7 * ## | | | | [9] V9 > 7 * ## | | [10] V4 > 1 ## | | | [11] V9 <= 0 * ## | | | [12] V9 > 0 * ## | [13] V5 > 1 ## | | [14] V2 <= 13 * ## | | [15] V2 > 13 * ## ## $nodes[[137]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V3 <= 71 ## | | | [4] V8 <= 925 * ## | | | [5] V8 > 925 ## | | | | [6] V3 <= 53 * ## | | | | [7] V3 > 53 ## | | | | | [8] V6 <= 80 * ## | | | | | [9] V6 > 80 * ## | | [10] V3 > 71 * ## | [11] V4 > 1 ## | | [12] V7 <= 90 ## | | | [13] V2 <= 15 ## | | | | [14] V5 <= 0 * ## | | | | [15] V5 > 0 * ## | | | [16] V2 > 15 * ## | | [17] V7 > 90 * ## ## $nodes[[138]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V4 <= 1 ## | | | [4] V3 <= 64 ## | | | | [5] V9 <= 2 * ## | | | | [6] V9 > 2 * ## | | | [7] V3 > 64 * ## | | [8] V4 > 1 ## | | | [9] V2 <= 11 * ## | | | [10] V2 > 11 * ## | [11] V5 > 1 ## | | [12] V4 <= 1 * ## | | [13] V4 > 1 * ## ## $nodes[[139]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V5 <= 1 ## | | | [4] V3 <= 68 ## | | | | [5] V6 <= 80 * ## | | | | [6] V6 > 80 ## | | | | | [7] V2 <= 5 * ## | | | | | [8] V2 > 5 * ## | | | [9] V3 > 68 * ## | | [10] V5 > 1 * ## | [11] V4 > 1 ## | | [12] V9 <= -1 * ## | | [13] V9 > -1 ## | | | [14] V7 <= 70 * ## | | | [15] V7 > 70 ## | | | | [16] V6 <= 80 * ## | | | | [17] V6 > 80 * ## ## $nodes[[140]] ## [1] root ## | [2] V6 <= 70 ## | | [3] V4 <= 1 * ## | | [4] V4 > 1 * ## | [5] V6 > 70 ## | | [6] V3 <= 64 ## | | | [7] V2 <= 10 ## | | | | [8] V7 <= 80 * ## | | | | [9] V7 > 80 * ## | | | [10] V2 > 10 ## | | | | [11] V6 <= 80 * ## | | | | [12] V6 > 80 * ## | | [13] V3 > 64 ## | | | [14] V8 <= 1030 * ## | | | [15] V8 > 1030 * ## ## $nodes[[141]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V7 <= 70 * ## | | [4] V7 > 70 ## | | | [5] V4 <= 1 ## | | | | [6] V6 <= 80 * ## | | | | [7] V6 > 80 ## | | | | | [8] V8 <= 1039 * ## | | | | | [9] V8 > 1039 * ## | | | [10] V4 > 1 ## | | | | [11] V3 <= 51 * ## | | | | [12] V3 > 51 ## | | | | | [13] V2 <= 6 * ## | | | | | [14] V2 > 6 * ## | [15] V5 > 1 ## | | [16] V2 <= 13 * ## | | [17] V2 > 13 * ## ## $nodes[[142]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V3 <= 64 ## | | | [4] V7 <= 90 ## | | | | [5] V7 <= 70 * ## | | | | [6] V7 > 70 ## | | | | | [7] V9 <= 1 * ## | | | | | [8] V9 > 1 * ## | | | [9] V7 > 90 * ## | | [10] V3 > 64 ## | | | [11] V5 <= 0 * ## | | | [12] V5 > 0 ## | | | | [13] V3 <= 69 * ## | | | | [14] V3 > 69 * ## | [15] V5 > 1 ## | | [16] V9 <= 20 * ## | | [17] V9 > 20 * ## ## $nodes[[143]] ## [1] root ## | [2] V6 <= 60 * ## | [3] V6 > 60 ## | | [4] V5 <= 1 ## | | | [5] V9 <= 8 ## | | | | [6] V7 <= 80 * ## | | | | [7] V7 > 80 ## | | | | | [8] V6 <= 90 ## | | | | | | [9] V8 <= 975 * ## | | | | | | [10] V8 > 975 * ## | | | | | [11] V6 > 90 * ## | | | [12] V9 > 8 ## | | | | [13] V4 <= 1 ## | | | | | [14] V2 <= 11 * ## | | | | | [15] V2 > 11 * ## | | | | [16] V4 > 1 * ## | | [17] V5 > 1 * ## ## $nodes[[144]] ## [1] root ## | [2] V3 <= 63 ## | | [3] V2 <= 10 ## | | | [4] V5 <= 0 * ## | | | [5] V5 > 0 * ## | | [6] V2 > 10 ## | | | [7] V8 <= 1025 * ## | | | [8] V8 > 1025 * ## | [9] V3 > 63 ## | | [10] V9 <= 27 ## | | | [11] V5 <= 1 ## | | | | [12] V5 <= 0 * ## | | | | [13] V5 > 0 ## | | | | | [14] V7 <= 70 * ## | | | | | [15] V7 > 70 * ## | | | [16] V5 > 1 * ## | | [17] V9 > 27 * ## ## $nodes[[145]] ## [1] root ## | [2] V6 <= 70 ## | | [3] V9 <= 20 ## | | | [4] V4 <= 1 * ## | | | [5] V4 > 1 * ## | | [6] V9 > 20 * ## | [7] V6 > 70 ## | | [8] V4 <= 1 ## | | | [9] V9 <= 6 * ## | | | [10] V9 > 6 ## | | | | [11] V7 <= 80 * ## | | | | [12] V7 > 80 * ## | | [13] V4 > 1 ## | | | [14] V9 <= 14 ## | | | | [15] V2 <= 11 * ## | | | | [16] V2 > 11 * ## | | | [17] V9 > 14 * ## ## $nodes[[146]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V7 <= 90 ## | | | [4] V5 <= 0 ## | | | | [5] V8 <= 775 * ## | | | | [6] V8 > 775 * ## | | | [7] V5 > 0 ## | | | | [8] V4 <= 1 ## | | | | | [9] V7 <= 80 * ## | | | | | [10] V7 > 80 * ## | | | | [11] V4 > 1 * ## | | [12] V7 > 90 * ## | [13] V5 > 1 ## | | [14] V6 <= 60 * ## | | [15] V6 > 60 * ## ## $nodes[[147]] ## [1] root ## | [2] V7 <= 60 * ## | [3] V7 > 60 ## | | [4] V5 <= 0 ## | | | [5] V3 <= 64 * ## | | | [6] V3 > 64 * ## | | [7] V5 > 0 ## | | | [8] V5 <= 1 ## | | | | [9] V6 <= 80 ## | | | | | [10] V2 <= 13 ## | | | | | | [11] V7 <= 70 * ## | | | | | | [12] V7 > 70 * ## | | | | | [13] V2 > 13 * ## | | | | [14] V6 > 80 * ## | | | [15] V5 > 1 * ## ## $nodes[[148]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V4 <= 1 ## | | | [4] V6 <= 80 * ## | | | [5] V6 > 80 ## | | | | [6] V8 <= 1175 ## | | | | | [7] V2 <= 11 * ## | | | | | [8] V2 > 11 * ## | | | | [9] V8 > 1175 * ## | | [10] V4 > 1 ## | | | [11] V3 <= 63 ## | | | | [12] V2 <= 10 * ## | | | | [13] V2 > 10 * ## | | | [14] V3 > 63 * ## | [15] V5 > 1 ## | | [16] V6 <= 60 * ## | | [17] V6 > 60 * ## ## $nodes[[149]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V5 <= 1 ## | | | [4] V2 <= 4 * ## | | | [5] V2 > 4 ## | | | | [6] V9 <= 15 ## | | | | | [7] V7 <= 90 ## | | | | | | [8] V8 <= 993 * ## | | | | | | [9] V8 > 993 * ## | | | | | [10] V7 > 90 * ## | | | | [11] V9 > 15 * ## | | [12] V5 > 1 * ## | [13] V4 > 1 ## | | [14] V5 <= 0 * ## | | [15] V5 > 0 ## | | | [16] V5 <= 1 * ## | | | [17] V5 > 1 * ## ## $nodes[[150]] ## [1] root ## | [2] V6 <= 70 ## | | [3] V7 <= 60 * ## | | [4] V7 > 60 * ## | [5] V6 > 70 ## | | [6] V5 <= 0 ## | | | [7] V7 <= 80 * ## | | | [8] V7 > 80 * ## | | [9] V5 > 0 ## | | | [10] V9 <= 14 ## | | | | [11] V7 <= 80 ## | | | | | [12] V2 <= 13 * ## | | | | | [13] V2 > 13 * ## | | | | [14] V7 > 80 * ## | | | [15] V9 > 14 * ## ## $nodes[[151]] ## [1] root ## | [2] V6 <= 70 ## | | [3] V2 <= 12 * ## | | [4] V2 > 12 * ## | [5] V6 > 70 ## | | [6] V4 <= 1 ## | | | [7] V3 <= 47 * ## | | | [8] V3 > 47 ## | | | | [9] V8 <= 1125 ## | | | | | [10] V3 <= 60 * ## | | | | | [11] V3 > 60 ## | | | | | | [12] V7 <= 80 * ## | | | | | | [13] V7 > 80 * ## | | | | [14] V8 > 1125 * ## | | [15] V4 > 1 ## | | | [16] V9 <= 0 * ## | | | [17] V9 > 0 * ## ## $nodes[[152]] ## [1] root ## | [2] V6 <= 70 ## | | [3] V7 <= 70 ## | | | [4] V9 <= 3 * ## | | | [5] V9 > 3 * ## | | [6] V7 > 70 * ## | [7] V6 > 70 ## | | [8] V4 <= 1 ## | | | [9] V5 <= 0 * ## | | | [10] V5 > 0 ## | | | | [11] V6 <= 80 * ## | | | | [12] V6 > 80 * ## | | [13] V4 > 1 ## | | | [14] V6 <= 90 ## | | | | [15] V7 <= 80 * ## | | | | [16] V7 > 80 * ## | | | [17] V6 > 90 * ## ## $nodes[[153]] ## [1] root ## | [2] V3 <= 65 ## | | [3] V7 <= 90 ## | | | [4] V9 <= 3 * ## | | | [5] V9 > 3 ## | | | | [6] V6 <= 70 * ## | | | | [7] V6 > 70 ## | | | | | [8] V2 <= 4 * ## | | | | | [9] V2 > 4 * ## | | [10] V7 > 90 * ## | [11] V3 > 65 ## | | [12] V3 <= 71 ## | | | [13] V8 <= 875 * ## | | | [14] V8 > 875 ## | | | | [15] V2 <= 12 * ## | | | | [16] V2 > 12 * ## | | [17] V3 > 71 * ## ## $nodes[[154]] ## [1] root ## | [2] V3 <= 44 * ## | [3] V3 > 44 ## | | [4] V5 <= 1 ## | | | [5] V9 <= 23 ## | | | | [6] V4 <= 1 ## | | | | | [7] V6 <= 90 ## | | | | | | [8] V2 <= 7 * ## | | | | | | [9] V2 > 7 * ## | | | | | [10] V6 > 90 * ## | | | | [11] V4 > 1 ## | | | | | [12] V2 <= 12 * ## | | | | | [13] V2 > 12 * ## | | | [14] V9 > 23 * ## | | [15] V5 > 1 ## | | | [16] V6 <= 60 * ## | | | [17] V6 > 60 * ## ## $nodes[[155]] ## [1] root ## | [2] V7 <= 60 * ## | [3] V7 > 60 ## | | [4] V4 <= 1 ## | | | [5] V3 <= 70 ## | | | | [6] V5 <= 0 * ## | | | | [7] V5 > 0 ## | | | | | [8] V2 <= 13 ## | | | | | | [9] V2 <= 11 * ## | | | | | | [10] V2 > 11 * ## | | | | | [11] V2 > 13 * ## | | | [12] V3 > 70 * ## | | [13] V4 > 1 ## | | | [14] V7 <= 90 ## | | | | [15] V2 <= 12 * ## | | | | [16] V2 > 12 * ## | | | [17] V7 > 90 * ## ## $nodes[[156]] ## [1] root ## | [2] V7 <= 90 ## | | [3] V3 <= 63 ## | | | [4] V7 <= 60 * ## | | | [5] V7 > 60 ## | | | | [6] V5 <= 0 * ## | | | | [7] V5 > 0 ## | | | | | [8] V4 <= 1 * ## | | | | | [9] V4 > 1 * ## | | [10] V3 > 63 ## | | | [11] V8 <= 1125 ## | | | | [12] V2 <= 15 ## | | | | | [13] V3 <= 71 ## | | | | | | [14] V3 <= 68 * ## | | | | | | [15] V3 > 68 * ## | | | | | [16] V3 > 71 * ## | | | | [17] V2 > 15 * ## | | | [18] V8 > 1125 * ## | [19] V7 > 90 * ## ## $nodes[[157]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V3 <= 60 ## | | | [4] V5 <= 0 * ## | | | [5] V5 > 0 * ## | | [6] V3 > 60 ## | | | [7] V9 <= 14 ## | | | | [8] V4 <= 1 ## | | | | | [9] V9 <= 2 * ## | | | | | [10] V9 > 2 * ## | | | | [11] V4 > 1 * ## | | | [12] V9 > 14 * ## | [13] V5 > 1 ## | | [14] V6 <= 60 * ## | | [15] V6 > 60 * ## ## $nodes[[158]] ## [1] root ## | [2] V6 <= 70 ## | | [3] V9 <= 16 * ## | | [4] V9 > 16 * ## | [5] V6 > 70 ## | | [6] V7 <= 60 * ## | | [7] V7 > 60 ## | | | [8] V4 <= 1 ## | | | | [9] V3 <= 60 * ## | | | | [10] V3 > 60 ## | | | | | [11] V3 <= 68 * ## | | | | | [12] V3 > 68 * ## | | | [13] V4 > 1 ## | | | | [14] V5 <= 0 * ## | | | | [15] V5 > 0 * ## ## $nodes[[159]] ## [1] root ## | [2] V3 <= 45 * ## | [3] V3 > 45 ## | | [4] V4 <= 1 ## | | | [5] V9 <= 27 ## | | | | [6] V6 <= 70 * ## | | | | [7] V6 > 70 ## | | | | | [8] V7 <= 90 ## | | | | | | [9] V8 <= 1100 ## | | | | | | | [10] V2 <= 11 * ## | | | | | | | [11] V2 > 11 * ## | | | | | | [12] V8 > 1100 * ## | | | | | [13] V7 > 90 * ## | | | [14] V9 > 27 * ## | | [15] V4 > 1 ## | | | [16] V7 <= 80 ## | | | | [17] V5 <= 1 * ## | | | | [18] V5 > 1 * ## | | | [19] V7 > 80 * ## ## $nodes[[160]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V3 <= 70 ## | | | [4] V6 <= 70 * ## | | | [5] V6 > 70 ## | | | | [6] V9 <= 6 * ## | | | | [7] V9 > 6 ## | | | | | [8] V8 <= 875 * ## | | | | | [9] V8 > 875 * ## | | [10] V3 > 70 * ## | [11] V4 > 1 ## | | [12] V9 <= 0 * ## | | [13] V9 > 0 ## | | | [14] V8 <= 825 * ## | | | [15] V8 > 825 * ## ## $nodes[[161]] ## [1] root ## | [2] V2 <= 15 ## | | [3] V5 <= 1 ## | | | [4] V5 <= 0 ## | | | | [5] V9 <= 5 * ## | | | | [6] V9 > 5 * ## | | | [7] V5 > 0 ## | | | | [8] V4 <= 1 ## | | | | | [9] V6 <= 80 * ## | | | | | [10] V6 > 80 * ## | | | | [11] V4 > 1 * ## | | [12] V5 > 1 * ## | [13] V2 > 15 ## | | [14] V2 <= 16 * ## | | [15] V2 > 16 * ## ## $nodes[[162]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V5 <= 0 ## | | | [4] V9 <= 6 ## | | | | [5] V2 <= 12 * ## | | | | [6] V2 > 12 * ## | | | [7] V9 > 6 * ## | | [8] V5 > 0 ## | | | [9] V8 <= 925 ## | | | | [10] V9 <= 14 * ## | | | | [11] V9 > 14 * ## | | | [12] V8 > 925 ## | | | | [13] V8 <= 1060 * ## | | | | [14] V8 > 1060 * ## | [15] V5 > 1 ## | | [16] V2 <= 7 * ## | | [17] V2 > 7 * ## ## $nodes[[163]] ## [1] root ## | [2] V3 <= 45 * ## | [3] V3 > 45 ## | | [4] V5 <= 0 ## | | | [5] V3 <= 64 * ## | | | [6] V3 > 64 * ## | | [7] V5 > 0 ## | | | [8] V9 <= 27 ## | | | | [9] V6 <= 70 ## | | | | | [10] V8 <= 1025 * ## | | | | | [11] V8 > 1025 * ## | | | | [12] V6 > 70 ## | | | | | [13] V4 <= 1 ## | | | | | | [14] V6 <= 80 * ## | | | | | | [15] V6 > 80 * ## | | | | | [16] V4 > 1 * ## | | | [17] V9 > 27 * ## ## $nodes[[164]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V4 <= 1 ## | | | [4] V7 <= 70 * ## | | | [5] V7 > 70 ## | | | | [6] V9 <= 12 ## | | | | | [7] V3 <= 60 * ## | | | | | [8] V3 > 60 * ## | | | | [9] V9 > 12 * ## | | [10] V4 > 1 ## | | | [11] V5 <= 0 * ## | | | [12] V5 > 0 * ## | [13] V5 > 1 ## | | [14] V9 <= 10 * ## | | [15] V9 > 10 * ## ## $nodes[[165]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V8 <= 1275 ## | | | [4] V6 <= 70 * ## | | | [5] V6 > 70 ## | | | | [6] V2 <= 11 ## | | | | | [7] V2 <= 6 * ## | | | | | [8] V2 > 6 * ## | | | | [9] V2 > 11 * ## | | [10] V8 > 1275 * ## | [11] V4 > 1 ## | | [12] V5 <= 0 * ## | | [13] V5 > 0 ## | | | [14] V2 <= 13 * ## | | | [15] V2 > 13 * ## ## $nodes[[166]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V3 <= 64 ## | | | [4] V5 <= 0 ## | | | | [5] V8 <= 1025 * ## | | | | [6] V8 > 1025 * ## | | | [7] V5 > 0 ## | | | | [8] V8 <= 875 * ## | | | | [9] V8 > 875 * ## | | [10] V3 > 64 ## | | | [11] V6 <= 80 * ## | | | [12] V6 > 80 * ## | [13] V5 > 1 ## | | [14] V2 <= 13 * ## | | [15] V2 > 13 * ## ## $nodes[[167]] ## [1] root ## | [2] V7 <= 70 ## | | [3] V6 <= 80 ## | | | [4] V5 <= 1 * ## | | | [5] V5 > 1 ## | | | | [6] V9 <= 20 * ## | | | | [7] V9 > 20 * ## | | [8] V6 > 80 * ## | [9] V7 > 70 ## | | [10] V5 <= 0 ## | | | [11] V2 <= 13 * ## | | | [12] V2 > 13 * ## | | [13] V5 > 0 ## | | | [14] V3 <= 53 * ## | | | [15] V3 > 53 ## | | | | [16] V6 <= 80 * ## | | | | [17] V6 > 80 * ## ## $nodes[[168]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V3 <= 71 ## | | | [4] V5 <= 1 ## | | | | [5] V8 <= 1150 ## | | | | | [6] V3 <= 64 * ## | | | | | [7] V3 > 64 * ## | | | | [8] V8 > 1150 * ## | | | [9] V5 > 1 * ## | | [10] V3 > 71 * ## | [11] V4 > 1 ## | | [12] V2 <= 10 * ## | | [13] V2 > 10 ## | | | [14] V9 <= 3 * ## | | | [15] V9 > 3 * ## ## $nodes[[169]] ## [1] root ## | [2] V3 <= 46 * ## | [3] V3 > 46 ## | | [4] V4 <= 1 ## | | | [5] V5 <= 1 ## | | | | [6] V7 <= 90 ## | | | | | [7] V8 <= 1125 ## | | | | | | [8] V2 <= 11 * ## | | | | | | [9] V2 > 11 * ## | | | | | [10] V8 > 1125 * ## | | | | [11] V7 > 90 * ## | | | [12] V5 > 1 * ## | | [13] V4 > 1 ## | | | [14] V5 <= 0 * ## | | | [15] V5 > 0 ## | | | | [16] V2 <= 16 * ## | | | | [17] V2 > 16 * ## ## $nodes[[170]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V3 <= 48 * ## | | [4] V3 > 48 ## | | | [5] V5 <= 1 ## | | | | [6] V7 <= 90 ## | | | | | [7] V7 <= 70 * ## | | | | | [8] V7 > 70 * ## | | | | [9] V7 > 90 * ## | | | [10] V5 > 1 * ## | [11] V4 > 1 ## | | [12] V5 <= 1 ## | | | [13] V7 <= 80 * ## | | | [14] V7 > 80 * ## | | [15] V5 > 1 * ## ## $nodes[[171]] ## [1] root ## | [2] V3 <= 66 ## | | [3] V4 <= 1 ## | | | [4] V7 <= 70 * ## | | | [5] V7 > 70 ## | | | | [6] V6 <= 80 * ## | | | | [7] V6 > 80 * ## | | [8] V4 > 1 ## | | | [9] V2 <= 16 ## | | | | [10] V5 <= 0 * ## | | | | [11] V5 > 0 * ## | | | [12] V2 > 16 * ## | [13] V3 > 66 ## | | [14] V4 <= 1 ## | | | [15] V5 <= 1 * ## | | | [16] V5 > 1 * ## | | [17] V4 > 1 * ## ## $nodes[[172]] ## [1] root ## | [2] V7 <= 70 ## | | [3] V9 <= 20 ## | | | [4] V4 <= 1 * ## | | | [5] V4 > 1 * ## | | [6] V9 > 20 * ## | [7] V7 > 70 ## | | [8] V3 <= 63 ## | | | [9] V4 <= 1 ## | | | | [10] V6 <= 80 * ## | | | | [11] V6 > 80 * ## | | | [12] V4 > 1 * ## | | [13] V3 > 63 ## | | | [14] V6 <= 80 * ## | | | [15] V6 > 80 * ## ## $nodes[[173]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V6 <= 80 ## | | | [4] V5 <= 1 * ## | | | [5] V5 > 1 * ## | | [6] V6 > 80 ## | | | [7] V9 <= -1 * ## | | | [8] V9 > -1 ## | | | | [9] V9 <= 4 * ## | | | | [10] V9 > 4 * ## | [11] V4 > 1 ## | | [12] V2 <= 11 ## | | | [13] V6 <= 80 * ## | | | [14] V6 > 80 * ## | | [15] V2 > 11 * ## ## $nodes[[174]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V5 <= 1 ## | | | [4] V9 <= 17 ## | | | | [5] V9 <= 8 ## | | | | | [6] V5 <= 0 * ## | | | | | [7] V5 > 0 * ## | | | | [8] V9 > 8 * ## | | | [9] V9 > 17 * ## | | [10] V5 > 1 * ## | [11] V4 > 1 ## | | [12] V7 <= 60 * ## | | [13] V7 > 60 ## | | | [14] V5 <= 0 * ## | | | [15] V5 > 0 ## | | | | [16] V8 <= 825 * ## | | | | [17] V8 > 825 * ## ## $nodes[[175]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V6 <= 80 ## | | | [4] V9 <= 20 ## | | | | [5] V5 <= 1 * ## | | | | [6] V5 > 1 * ## | | | [7] V9 > 20 * ## | | [8] V6 > 80 ## | | | [9] V9 <= 6 * ## | | | [10] V9 > 6 * ## | [11] V4 > 1 ## | | [12] V9 <= -1 * ## | | [13] V9 > -1 ## | | | [14] V5 <= 0 * ## | | | [15] V5 > 0 ## | | | | [16] V6 <= 70 * ## | | | | [17] V6 > 70 * ## ## $nodes[[176]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V3 <= 64 ## | | | [4] V2 <= 12 ## | | | | [5] V3 <= 50 * ## | | | | [6] V3 > 50 ## | | | | | [7] V5 <= 0 * ## | | | | | [8] V5 > 0 * ## | | | [9] V2 > 12 * ## | | [10] V3 > 64 ## | | | [11] V9 <= 10 ## | | | | [12] V5 <= 0 * ## | | | | [13] V5 > 0 * ## | | | [14] V9 > 10 * ## | [15] V5 > 1 ## | | [16] V6 <= 60 * ## | | [17] V6 > 60 * ## ## $nodes[[177]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V2 <= 15 ## | | | [4] V5 <= 1 ## | | | | [5] V8 <= 1175 ## | | | | | [6] V6 <= 80 * ## | | | | | [7] V6 > 80 * ## | | | | [8] V8 > 1175 * ## | | | [9] V5 > 1 * ## | | [10] V2 > 15 * ## | [11] V4 > 1 ## | | [12] V6 <= 70 * ## | | [13] V6 > 70 ## | | | [14] V8 <= 975 * ## | | | [15] V8 > 975 * ## ## $nodes[[178]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V4 <= 1 ## | | | [4] V3 <= 63 ## | | | | [5] V5 <= 0 * ## | | | | [6] V5 > 0 * ## | | | [7] V3 > 63 ## | | | | [8] V9 <= 17 ## | | | | | [9] V6 <= 80 * ## | | | | | [10] V6 > 80 * ## | | | | [11] V9 > 17 * ## | | [12] V4 > 1 ## | | | [13] V5 <= 0 * ## | | | [14] V5 > 0 * ## | [15] V5 > 1 ## | | [16] V3 <= 62 * ## | | [17] V3 > 62 * ## ## $nodes[[179]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V3 <= 51 * ## | | [4] V3 > 51 ## | | | [5] V5 <= 0 ## | | | | [6] V9 <= 5 * ## | | | | [7] V9 > 5 * ## | | | [8] V5 > 0 ## | | | | [9] V6 <= 80 ## | | | | | [10] V9 <= 14 * ## | | | | | [11] V9 > 14 * ## | | | | [12] V6 > 80 ## | | | | | [13] V8 <= 875 * ## | | | | | [14] V8 > 875 * ## | [15] V5 > 1 ## | | [16] V8 <= 413 * ## | | [17] V8 > 413 * ## ## $nodes[[180]] ## [1] root ## | [2] V7 <= 60 * ## | [3] V7 > 60 ## | | [4] V4 <= 1 ## | | | [5] V7 <= 90 ## | | | | [6] V8 <= 910 * ## | | | | [7] V8 > 910 ## | | | | | [8] V9 <= -1 * ## | | | | | [9] V9 > -1 ## | | | | | | [10] V2 <= 11 * ## | | | | | | [11] V2 > 11 * ## | | | [12] V7 > 90 * ## | | [13] V4 > 1 ## | | | [14] V7 <= 80 * ## | | | [15] V7 > 80 ## | | | | [16] V9 <= 6 * ## | | | | [17] V9 > 6 * ## ## $nodes[[181]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V7 <= 60 * ## | | [4] V7 > 60 ## | | | [5] V6 <= 70 * ## | | | [6] V6 > 70 ## | | | | [7] V3 <= 63 ## | | | | | [8] V3 <= 58 * ## | | | | | [9] V3 > 58 * ## | | | | [10] V3 > 63 * ## | [11] V4 > 1 ## | | [12] V7 <= 80 ## | | | [13] V3 <= 60 * ## | | | [14] V3 > 60 * ## | | [15] V7 > 80 * ## ## $nodes[[182]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V4 <= 1 ## | | | [4] V8 <= 925 * ## | | | [5] V8 > 925 ## | | | | [6] V5 <= 0 * ## | | | | [7] V5 > 0 * ## | | [8] V4 > 1 ## | | | [9] V8 <= 925 * ## | | | [10] V8 > 925 * ## | [11] V5 > 1 ## | | [12] V9 <= 11 * ## | | [13] V9 > 11 * ## ## $nodes[[183]] ## [1] root ## | [2] V5 <= 0 ## | | [3] V8 <= 588 * ## | | [4] V8 > 588 ## | | | [5] V7 <= 80 * ## | | | [6] V7 > 80 * ## | [7] V5 > 0 ## | | [8] V2 <= 12 ## | | | [9] V6 <= 70 * ## | | | [10] V6 > 70 ## | | | | [11] V3 <= 64 * ## | | | | [12] V3 > 64 * ## | | [13] V2 > 12 ## | | | [14] V9 <= 15 ## | | | | [15] V4 <= 1 * ## | | | | [16] V4 > 1 * ## | | | [17] V9 > 15 * ## ## $nodes[[184]] ## [1] root ## | [2] V3 <= 65 ## | | [3] V6 <= 70 * ## | | [4] V6 > 70 ## | | | [5] V8 <= 1060 ## | | | | [6] V6 <= 80 * ## | | | | [7] V6 > 80 ## | | | | | [8] V7 <= 80 * ## | | | | | [9] V7 > 80 * ## | | | [10] V8 > 1060 * ## | [11] V3 > 65 ## | | [12] V4 <= 1 ## | | | [13] V5 <= 1 * ## | | | [14] V5 > 1 * ## | | [15] V4 > 1 * ## ## $nodes[[185]] ## [1] root ## | [2] V6 <= 70 ## | | [3] V4 <= 1 * ## | | [4] V4 > 1 * ## | [5] V6 > 70 ## | | [6] V4 <= 1 ## | | | [7] V9 <= 2 * ## | | | [8] V9 > 2 ## | | | | [9] V5 <= 0 * ## | | | | [10] V5 > 0 ## | | | | | [11] V7 <= 70 * ## | | | | | [12] V7 > 70 * ## | | [13] V4 > 1 ## | | | [14] V3 <= 59 * ## | | | [15] V3 > 59 * ## ## $nodes[[186]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V8 <= 575 * ## | | [4] V8 > 575 ## | | | [5] V8 <= 1025 ## | | | | [6] V9 <= 7 ## | | | | | [7] V4 <= 1 * ## | | | | | [8] V4 > 1 * ## | | | | [9] V9 > 7 * ## | | | [10] V8 > 1025 ## | | | | [11] V4 <= 1 ## | | | | | [12] V9 <= 2 * ## | | | | | [13] V9 > 2 * ## | | | | [14] V4 > 1 * ## | [15] V5 > 1 ## | | [16] V2 <= 13 * ## | | [17] V2 > 13 * ## ## $nodes[[187]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V3 <= 71 ## | | | [4] V9 <= 15 ## | | | | [5] V9 <= -1 * ## | | | | [6] V9 > -1 ## | | | | | [7] V2 <= 5 * ## | | | | | [8] V2 > 5 ## | | | | | | [9] V9 <= 8 * ## | | | | | | [10] V9 > 8 * ## | | | [11] V9 > 15 * ## | | [12] V3 > 71 * ## | [13] V4 > 1 ## | | [14] V9 <= 13 ## | | | [15] V5 <= 0 * ## | | | [16] V5 > 0 * ## | | [17] V9 > 13 * ## ## $nodes[[188]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V5 <= 0 * ## | | [4] V5 > 0 ## | | | [5] V9 <= 24 ## | | | | [6] V5 <= 1 ## | | | | | [7] V7 <= 70 * ## | | | | | [8] V7 > 70 * ## | | | | [9] V5 > 1 * ## | | | [10] V9 > 24 * ## | [11] V4 > 1 ## | | [12] V5 <= 0 * ## | | [13] V5 > 0 ## | | | [14] V2 <= 12 * ## | | | [15] V2 > 12 * ## ## $nodes[[189]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V7 <= 60 * ## | | [4] V7 > 60 ## | | | [5] V5 <= 0 * ## | | | [6] V5 > 0 ## | | | | [7] V3 <= 70 ## | | | | | [8] V8 <= 993 * ## | | | | | [9] V8 > 993 * ## | | | | [10] V3 > 70 * ## | [11] V4 > 1 ## | | [12] V7 <= 90 ## | | | [13] V7 <= 80 * ## | | | [14] V7 > 80 * ## | | [15] V7 > 90 * ## ## $nodes[[190]] ## [1] root ## | [2] V3 <= 70 ## | | [3] V7 <= 90 ## | | | [4] V4 <= 1 ## | | | | [5] V5 <= 0 * ## | | | | [6] V5 > 0 ## | | | | | [7] V8 <= 1025 * ## | | | | | [8] V8 > 1025 * ## | | | [9] V4 > 1 ## | | | | [10] V5 <= 0 * ## | | | | [11] V5 > 0 * ## | | [12] V7 > 90 * ## | [13] V3 > 70 ## | | [14] V5 <= 1 * ## | | [15] V5 > 1 * ## ## $nodes[[191]] ## [1] root ## | [2] V3 <= 71 ## | | [3] V2 <= 21 ## | | | [4] V6 <= 70 * ## | | | [5] V6 > 70 ## | | | | [6] V3 <= 64 ## | | | | | [7] V5 <= 0 * ## | | | | | [8] V5 > 0 ## | | | | | | [9] V7 <= 80 * ## | | | | | | [10] V7 > 80 * ## | | | | [11] V3 > 64 * ## | | [12] V2 > 21 * ## | [13] V3 > 71 * ## ## $nodes[[192]] ## [1] root ## | [2] V6 <= 70 ## | | [3] V2 <= 7 * ## | | [4] V2 > 7 * ## | [5] V6 > 70 ## | | [6] V9 <= 5 ## | | | [7] V7 <= 90 ## | | | | [8] V6 <= 80 * ## | | | | [9] V6 > 80 * ## | | | [10] V7 > 90 * ## | | [11] V9 > 5 ## | | | [12] V3 <= 64 ## | | | | [13] V3 <= 56 * ## | | | | [14] V3 > 56 * ## | | | [15] V3 > 64 * ## ## $nodes[[193]] ## [1] root ## | [2] V5 <= 0 ## | | [3] V9 <= 3 * ## | | [4] V9 > 3 ## | | | [5] V2 <= 4 * ## | | | [6] V2 > 4 * ## | [7] V5 > 0 ## | | [8] V7 <= 60 * ## | | [9] V7 > 60 ## | | | [10] V2 <= 5 * ## | | | [11] V2 > 5 ## | | | | [12] V9 <= 7 * ## | | | | [13] V9 > 7 * ## ## $nodes[[194]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V5 <= 0 ## | | | [4] V7 <= 80 * ## | | | [5] V7 > 80 * ## | | [6] V5 > 0 ## | | | [7] V4 <= 1 ## | | | | [8] V6 <= 80 ## | | | | | [9] V9 <= 15 * ## | | | | | [10] V9 > 15 * ## | | | | [11] V6 > 80 * ## | | | [12] V4 > 1 ## | | | | [13] V7 <= 80 * ## | | | | [14] V7 > 80 * ## | [15] V5 > 1 ## | | [16] V2 <= 13 * ## | | [17] V2 > 13 * ## ## $nodes[[195]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V9 <= 27 ## | | | [4] V5 <= 1 ## | | | | [5] V2 <= 11 ## | | | | | [6] V7 <= 70 * ## | | | | | [7] V7 > 70 * ## | | | | [8] V2 > 11 * ## | | | [9] V5 > 1 * ## | | [10] V9 > 27 * ## | [11] V4 > 1 ## | | [12] V2 <= 12 ## | | | [13] V6 <= 80 * ## | | | [14] V6 > 80 * ## | | [15] V2 > 12 * ## ## $nodes[[196]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V6 <= 70 * ## | | [4] V6 > 70 ## | | | [5] V5 <= 0 * ## | | | [6] V5 > 0 ## | | | | [7] V3 <= 59 * ## | | | | [8] V3 > 59 ## | | | | | [9] V9 <= 8 * ## | | | | | [10] V9 > 8 * ## | [11] V4 > 1 ## | | [12] V7 <= 80 ## | | | [13] V9 <= 0 * ## | | | [14] V9 > 0 * ## | | [15] V7 > 80 * ## ## $nodes[[197]] ## [1] root ## | [2] V7 <= 60 ## | | [3] V5 <= 1 * ## | | [4] V5 > 1 * ## | [5] V7 > 60 ## | | [6] V4 <= 1 ## | | | [7] V8 <= 488 * ## | | | [8] V8 > 488 ## | | | | [9] V2 <= 15 ## | | | | | [10] V5 <= 0 * ## | | | | | [11] V5 > 0 * ## | | | | [12] V2 > 15 * ## | | [13] V4 > 1 ## | | | [14] V5 <= 0 * ## | | | [15] V5 > 0 ## | | | | [16] V3 <= 65 * ## | | | | [17] V3 > 65 * ## ## $nodes[[198]] ## [1] root ## | [2] V5 <= 1 ## | | [3] V4 <= 1 ## | | | [4] V6 <= 80 ## | | | | [5] V9 <= 14 * ## | | | | [6] V9 > 14 * ## | | | [7] V6 > 80 ## | | | | [8] V9 <= 6 * ## | | | | [9] V9 > 6 * ## | | [10] V4 > 1 ## | | | [11] V6 <= 80 * ## | | | [12] V6 > 80 * ## | [13] V5 > 1 ## | | [14] V9 <= 10 * ## | | [15] V9 > 10 * ## ## $nodes[[199]] ## [1] root ## | [2] V4 <= 1 ## | | [3] V5 <= 1 ## | | | [4] V3 <= 65 ## | | | | [5] V9 <= 1 * ## | | | | [6] V9 > 1 ## | | | | | [7] V3 <= 56 * ## | | | | | [8] V3 > 56 * ## | | | [9] V3 > 65 * ## | | [10] V5 > 1 * ## 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153 153 222+ ## 154 154 183 ## 155 155 211+ ## 156 156 175+ ## 157 157 197+ ## 158 158 203+ ## 159 159 191+ ## 160 160 105+ ## 161 161 174+ ## 162 162 177+ ## ## $terms ## Surv(time, status) ~ inst + age + sex + ph.ecog + ph.karno + ## pat.karno + meal.cal + wt.loss ## attr(,\"variables\") ## list(Surv(time, status), inst, age, sex, ph.ecog, ph.karno, pat.karno, ## meal.cal, wt.loss) ## attr(,\"factors\") ## inst age sex ph.ecog ph.karno pat.karno meal.cal ## Surv(time, status) 0 0 0 0 0 0 0 ## inst 1 0 0 0 0 0 0 ## age 0 1 0 0 0 0 0 ## sex 0 0 1 0 0 0 0 ## ph.ecog 0 0 0 1 0 0 0 ## ph.karno 0 0 0 0 1 0 0 ## pat.karno 0 0 0 0 0 1 0 ## meal.cal 0 0 0 0 0 0 1 ## wt.loss 0 0 0 0 0 0 0 ## wt.loss ## Surv(time, status) 0 ## inst 0 ## age 0 ## sex 0 ## ph.ecog 0 ## ph.karno 0 ## pat.karno 0 ## meal.cal 0 ## wt.loss 1 ## attr(,\"term.labels\") ## [1] \"inst\" \"age\" \"sex\" \"ph.ecog\" \"ph.karno\" ## [6] \"pat.karno\" \"meal.cal\" \"wt.loss\" ## attr(,\"order\") ## [1] 1 1 1 1 1 1 1 1 ## attr(,\"intercept\") ## [1] 1 ## attr(,\"response\") ## [1] 1 ## attr(,\".Environment\") ## ## attr(,\"Formula_with_dot\") ## Surv(time, status) ~ . ## ## attr(,\"Formula_without_dot\") ## Surv(time, status) ~ inst + age + sex + ph.ecog + ph.karno + ## pat.karno + meal.cal + wt.loss ## ## attr(,\"dot\") ## [1] \"sequential\" ## ## $info ## $info$call ## partykit::cforest(formula = formula, data = data, weights = weights, ## control = partykit::ctree_control(minsplit = 20L, maxdepth = Inf, ## teststat = \"quadratic\", testtype = \"Univariate\", mincriterion = 0, ## saveinfo = FALSE), ntree = 200, mtry = 3) ## ## $info$control ## $info$control$criterion ## [1] \"p.value\" ## ## $info$control$logmincriterion ## [1] -Inf ## ## $info$control$minsplit ## [1] 20 ## ## $info$control$minbucket ## [1] 7 ## ## $info$control$minprob ## [1] 0.01 ## ## $info$control$maxvar ## [1] Inf ## ## $info$control$stump ## [1] FALSE ## ## $info$control$nmax ## yx z ## Inf Inf ## ## $info$control$lookahead ## [1] FALSE ## ## $info$control$mtry ## [1] 3 ## ## $info$control$maxdepth ## [1] Inf ## ## $info$control$multiway ## [1] FALSE ## ## $info$control$splittry ## [1] 2 ## ## $info$control$maxsurrogate ## [1] 0 ## ## $info$control$numsurrogate ## [1] FALSE ## ## $info$control$majority ## [1] FALSE ## ## $info$control$caseweights ## [1] TRUE ## ## $info$control$applyfun ## function (X, FUN, ...) ## { ## FUN <- match.fun(FUN) ## if (!is.vector(X) || is.object(X)) ## X <- as.list(X) ## .Internal(lapply(X, FUN)) ## } ## ## ## ## $info$control$saveinfo ## [1] FALSE ## ## $info$control$bonferroni ## [1] FALSE ## ## $info$control$update ## [1] FALSE ## ## $info$control$selectfun ## function (model, trafo, data, subset, weights, whichvar, ctrl) ## { ## args <- list(...) ## ctrl[names(args)] <- args ## .select(model, trafo, data, subset, weights, whichvar, ctrl, ## FUN = .ctree_test) ## } ## ## ## ## $info$control$splitfun ## function (model, trafo, data, subset, weights, whichvar, ctrl) ## { ## args <- list(...) ## ctrl[names(args)] <- args ## .split(model, trafo, data, subset, weights, whichvar, ctrl, ## FUN = .ctree_test) ## } ## ## ## ## $info$control$svselectfun ## function (model, trafo, data, subset, weights, whichvar, ctrl) ## { ## args <- list(...) ## ctrl[names(args)] <- args ## .select(model, trafo, data, subset, weights, whichvar, ctrl, ## FUN = .ctree_test) ## } ## ## ## ## $info$control$svsplitfun ## function (model, trafo, data, subset, weights, whichvar, ctrl) ## { ## args <- list(...) ## ctrl[names(args)] <- args ## .split(model, trafo, data, subset, weights, whichvar, ctrl, ## FUN = .ctree_test) ## } ## ## ## ## $info$control$teststat ## [1] \"quadratic\" ## ## $info$control$splitstat ## [1] \"quadratic\" ## ## $info$control$splittest ## [1] FALSE ## ## $info$control$pargs ## $maxpts ## [1] 25000 ## ## $abseps ## [1] 0.001 ## ## $releps ## [1] 0 ## ## attr(,\"class\") ## [1] \"GenzBretz\" ## ## $info$control$testtype ## [1] \"Univariate\" ## ## $info$control$nresample ## [1] 9999 ## ## $info$control$tol ## [1] 1.490116e-08 ## ## $info$control$intersplit ## [1] FALSE ## ## $info$control$MIA ## [1] FALSE ## ## ## ## $trafo ## function (subset, weights, info, estfun, object, ...) ## list(estfun = Y, unweighted = TRUE) ## ## ## ## $predictf ## ~inst + age + sex + ph.ecog + ph.karno + pat.karno + meal.cal + ## wt.loss ## attr(,\"variables\") ## list(inst, age, sex, ph.ecog, ph.karno, pat.karno, meal.cal, ## wt.loss) ## attr(,\"factors\") ## inst age sex ph.ecog ph.karno pat.karno meal.cal wt.loss ## inst 1 0 0 0 0 0 0 0 ## age 0 1 0 0 0 0 0 0 ## sex 0 0 1 0 0 0 0 0 ## ph.ecog 0 0 0 1 0 0 0 0 ## ph.karno 0 0 0 0 1 0 0 0 ## pat.karno 0 0 0 0 0 1 0 0 ## meal.cal 0 0 0 0 0 0 1 0 ## wt.loss 0 0 0 0 0 0 0 1 ## attr(,\"term.labels\") ## [1] \"inst\" \"age\" \"sex\" \"ph.ecog\" \"ph.karno\" ## [6] \"pat.karno\" \"meal.cal\" \"wt.loss\" ## attr(,\"order\") ## [1] 1 1 1 1 1 1 1 1 ## attr(,\"intercept\") ## [1] 1 ## attr(,\"response\") ## [1] 0 ## attr(,\".Environment\") ## ## attr(,\"Formula_with_dot\") ## Surv(time, status) ~ . ## ## attr(,\"Formula_without_dot\") ## Surv(time, status) ~ inst + age + sex + ph.ecog + ph.karno + ## pat.karno + meal.cal + wt.loss ## ## attr(,\"dot\") ## [1] \"sequential\" ## ## attr(,\"class\") ## [1] \"cforest\" \"constparties\" \"parties\" predict( rf_fit, lung_test, type = \"survival\", eval_time = c(100, 500, 1000) ) %>% slice(1) %>% tidyr::unnest(col = .pred) ## # A tibble: 3 × 2 ## .eval_time .pred_survival ## ## 1 100 0.886 ## 2 500 0.303 ## 3 1000 0.0443 predict(rf_fit, lung_test, type = \"time\") ## # A tibble: 5 × 1 ## .pred_time ## ## 1 337 ## 2 267 ## 3 230 ## 4 201 ## 5 226 library(tidymodels) library(censored) tidymodels_prefer() data(cancer) lung <- lung %>% drop_na() lung_train <- lung[-c(1:5), ] lung_test <- lung[1:5, ] rf_spec <- rand_forest(trees = 200) %>% set_engine(\"aorsf\") %>% set_mode(\"censored regression\") rf_spec ## Random Forest Model Specification (censored regression) ## ## Main Arguments: ## trees = 200 ## ## Computational engine: aorsf set.seed(1) rf_fit <- rf_spec %>% fit(Surv(time, status) ~ ., data = lung_train) rf_fit ## parsnip model object ## ## ---------- Oblique random survival forest ## ## Linear combinations: Accelerated Cox regression ## N observations: 162 ## N events: 116 ## N trees: 200 ## N predictors total: 8 ## N predictors per node: 3 ## Average leaves per tree: 17 ## Min observations in leaf: 5 ## Min events in leaf: 1 ## OOB stat value: 0.61 ## OOB stat type: Harrell's C-statistic ## Variable importance: anova ## ## ----------------------------------------- predict( rf_fit, lung_test, type = \"survival\", eval_time = c(100, 500, 1000) ) %>% slice(1) %>% tidyr::unnest(col = .pred) ## # A tibble: 3 × 2 ## .eval_time .pred_survival ## ## 1 100 0.931 ## 2 500 0.399 ## 3 1000 0.0624"},{"path":"https://censored.tidymodels.org/dev/articles/examples.html","id":"survival_reg-models","dir":"Articles","previous_headings":"","what":"survival_reg() models","title":"Fitting and Predicting with censored","text":"’ll model survival lung cancer patients. can define model specific parameters: Now create model fit object: holdout data can predicted survival probability different time points well event time, linear predictor, quantile, hazard. ’ll model survival lung cancer patients. can define model specific parameters: Now create model fit object: holdout data can predicted survival probability different time points well event time, linear predictor, quantile, hazard. ’ll model survival lung cancer patients. can define model: Now create model fit object: holdout data can predicted survival probability different time points well event time, linear predictor, quantile, hazard.","code":"library(tidymodels) library(censored) tidymodels_prefer() data(cancer) lung <- lung %>% drop_na() lung_train <- lung[-c(1:5), ] lung_test <- lung[1:5, ] sr_spec <- survival_reg(dist = \"weibull\") %>% set_engine(\"survival\") %>% set_mode(\"censored regression\") sr_spec ## Parametric Survival Regression Model Specification (censored regression) ## ## Main Arguments: ## dist = weibull ## ## Computational engine: survival set.seed(1) sr_fit <- sr_spec %>% fit(Surv(time, status) ~ ., data = lung_train) sr_fit ## parsnip model object ## ## Call: ## survival::survreg(formula = Surv(time, status) ~ ., data = data, ## dist = ~\"weibull\", model = TRUE) ## ## Coefficients: ## (Intercept) inst age sex ph.ecog ## 6.2802499155 0.0191302849 -0.0085917372 0.4249655608 -0.5022975982 ## ph.karno pat.karno meal.cal wt.loss ## -0.0085852225 0.0058753359 0.0001003211 0.0127001420 ## ## Scale= 0.6902035 ## ## Loglik(model)= -795.2 Loglik(intercept only)= -811.4 ## Chisq= 32.41 on 8 degrees of freedom, p= 7.85e-05 ## n= 162 predict( sr_fit, lung_test, type = \"survival\", eval_time = c(100, 500, 1000) ) %>% slice(1) %>% tidyr::unnest(col = .pred) ## # A tibble: 3 × 2 ## .eval_time .pred_survival ## ## 1 100 0.912 ## 2 500 0.386 ## 3 1000 0.0742 predict(sr_fit, lung_test, type = \"time\") ## # A tibble: 5 × 1 ## .pred_time ## ## 1 517. ## 2 283. ## 3 361. ## 4 268. ## 5 313. predict(sr_fit, lung_test, type = \"linear_pred\") ## # A tibble: 5 × 1 ## .pred_linear_pred ## ## 1 6.25 ## 2 5.64 ## 3 5.89 ## 4 5.59 ## 5 5.75 predict(sr_fit, lung_test, type = \"quantile\") %>% slice(1) %>% tidyr::unnest(col = .pred) ## # A tibble: 9 × 2 ## .quantile .pred_quantile ## ## 1 0.1 109. ## 2 0.2 184. ## 3 0.3 254. ## 4 0.4 325. ## 5 0.5 401. ## 6 0.6 487. ## 7 0.7 588. ## 8 0.8 718. ## 9 0.9 919. predict(sr_fit, lung_test, type = \"hazard\", eval_time = c(100, 500, 1000)) %>% slice(1) %>% tidyr::unnest(col = .pred) ## # A tibble: 3 × 2 ## .eval_time .pred_hazard ## ## 1 100 0.00134 ## 2 500 0.00276 ## 3 1000 0.00377 library(tidymodels) library(censored) tidymodels_prefer() data(cancer) lung <- lung %>% drop_na() lung_train <- lung[-c(1:5), ] lung_test <- lung[1:5, ] sr_spec <- survival_reg(dist = \"weibull\") %>% set_engine(\"flexsurv\") %>% set_mode(\"censored regression\") sr_spec ## Parametric Survival Regression Model Specification (censored regression) ## ## Main Arguments: ## dist = weibull ## ## Computational engine: flexsurv set.seed(1) sr_fit <- sr_spec %>% fit(Surv(time, status) ~ age + sex + ph.ecog, data = lung_train) sr_fit ## parsnip model object ## ## Call: ## flexsurv::flexsurvreg(formula = Surv(time, status) ~ age + sex + ## ph.ecog, data = data, dist = ~\"weibull\") ## ## Estimates: ## data mean est L95% U95% se exp(est) ## shape NA 1.39e+00 1.21e+00 1.61e+00 1.02e-01 NA ## scale NA 5.74e+02 1.99e+02 1.65e+03 3.10e+02 NA ## age 6.24e+01 -9.02e-03 -2.50e-02 6.93e-03 8.14e-03 9.91e-01 ## sex 1.38e+00 4.02e-01 1.17e-01 6.87e-01 1.45e-01 1.50e+00 ## ph.ecog 9.51e-01 -3.17e-01 -5.13e-01 -1.21e-01 1.00e-01 7.28e-01 ## L95% U95% ## shape NA NA ## scale NA NA ## age 9.75e-01 1.01e+00 ## sex 1.12e+00 1.99e+00 ## ph.ecog 5.99e-01 8.86e-01 ## ## N = 162, Events: 116, Censored: 46 ## Total time at risk: 49401 ## Log-likelihood = -800.356, df = 5 ## AIC = 1610.712 predict( sr_fit, lung_test, type = \"survival\", eval_time = c(100, 500, 1000) ) %>% slice(1) %>% tidyr::unnest(col = .pred) ## # A tibble: 3 × 2 ## .eval_time .pred_survival ## ## 1 100 0.889 ## 2 500 0.330 ## 3 1000 0.0543 predict(sr_fit, lung_test, type = \"time\") ## # A tibble: 5 × 1 ## .pred_time ## ## 1 424. ## 2 341. ## 3 292. ## 4 336. ## 5 327. predict(sr_fit, lung_test, type = \"linear_pred\") ## # A tibble: 5 × 1 ## .pred_linear_pred ## ## 1 6.14 ## 2 5.92 ## 3 5.77 ## 4 5.91 ## 5 5.88 predict(sr_fit, lung_test, type = \"quantile\") %>% slice(1) %>% tidyr::unnest(col = .pred) ## # A tibble: 9 × 2 ## .quantile .pred_quantile ## ## 1 0.1 92.5 ## 2 0.2 158. ## 3 0.3 222. ## 4 0.4 287. ## 5 0.5 357. ## 6 0.6 436. ## 7 0.7 531. ## 8 0.8 653. ## 9 0.9 845. predict(sr_fit, lung_test, type = \"hazard\", eval_time = c(100, 500, 1000)) %>% slice(1) %>% tidyr::unnest(col = .pred) ## # A tibble: 3 × 2 ## .eval_time .pred_hazard ## ## 1 100 0.00164 ## 2 500 0.00309 ## 3 1000 0.00406 library(tidymodels) library(censored) tidymodels_prefer() data(cancer) lung <- lung %>% drop_na() lung_train <- lung[-c(1:5), ] lung_test <- lung[1:5, ] sr_spec <- survival_reg() %>% set_engine(\"flexsurvspline\") %>% set_mode(\"censored regression\") sr_spec ## Parametric Survival Regression Model Specification (censored regression) ## ## Computational engine: flexsurvspline set.seed(1) sr_fit <- sr_spec %>% fit(Surv(time, status) ~ age + sex + ph.ecog, data = lung_train) sr_fit ## parsnip model object ## ## Call: ## flexsurv::flexsurvspline(formula = Surv(time, status) ~ age + ## sex + ph.ecog, data = data) ## ## Estimates: ## data mean est L95% U95% se exp(est) ## gamma0 NA -8.85681 -10.78535 -6.92827 0.98397 NA ## gamma1 NA 1.39431 1.19358 1.59504 0.10241 NA ## age 62.41358 0.01258 -0.00966 0.03482 0.01135 1.01266 ## sex 1.38272 -0.56080 -0.95517 -0.16643 0.20121 0.57075 ## ph.ecog 0.95062 0.44213 0.17197 0.71230 0.13784 1.55602 ## L95% U95% ## gamma0 NA NA ## gamma1 NA NA ## age 0.99039 1.03543 ## sex 0.38475 0.84668 ## ph.ecog 1.18764 2.03867 ## ## N = 162, Events: 116, Censored: 46 ## Total time at risk: 49401 ## Log-likelihood = -800.356, df = 5 ## AIC = 1610.712 predict( sr_fit, lung_test, type = \"survival\", eval_time = c(100, 500, 1000) ) %>% slice(1) %>% tidyr::unnest(col = .pred) ## # A tibble: 3 × 2 ## .eval_time .pred_survival ## ## 1 100 0.889 ## 2 500 0.330 ## 3 1000 0.0543 predict(sr_fit, lung_test, type = \"time\") ## # A tibble: 5 × 1 ## .pred_time ## ## 1 424. ## 2 341. ## 3 292. ## 4 336. ## 5 327. predict(sr_fit, lung_test, type = \"linear_pred\") ## # A tibble: 5 × 1 ## .pred_linear_pred ## ## 1 -8.56 ## 2 -8.26 ## 3 -8.04 ## 4 -8.24 ## 5 -8.20 predict(sr_fit, lung_test, type = \"quantile\") %>% slice(1) %>% tidyr::unnest(col = .pred) ## # A tibble: 9 × 2 ## .quantile .pred_quantile ## ## 1 0.1 92.5 ## 2 0.2 158. ## 3 0.3 222. ## 4 0.4 287. ## 5 0.5 357. ## 6 0.6 436. ## 7 0.7 531. ## 8 0.8 653. ## 9 0.9 845. predict(sr_fit, lung_test, type = \"hazard\", eval_time = c(100, 500, 1000)) %>% slice(1) %>% tidyr::unnest(col = .pred) ## # A tibble: 3 × 2 ## .eval_time .pred_hazard ## ## 1 100 0.00164 ## 2 500 0.00309 ## 3 1000 0.00406"},{"path":"https://censored.tidymodels.org/dev/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Emil Hvitfeldt. Author. Hannah Frick. Author, maintainer. . Copyright holder, funder.","code":""},{"path":"https://censored.tidymodels.org/dev/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Hvitfeldt E, Frick H (2023). censored: 'parsnip' Engines Survival Models. R package version 0.2.0.9001, https://censored.tidymodels.org, https://github.com/tidymodels/censored.","code":"@Manual{, title = {censored: 'parsnip' Engines for Survival Models}, author = {Emil Hvitfeldt and Hannah Frick}, year = {2023}, note = {R package version 0.2.0.9001, https://censored.tidymodels.org}, url = {https://github.com/tidymodels/censored}, }"},{"path":"https://censored.tidymodels.org/dev/index.html","id":"censored-","dir":"","previous_headings":"","what":"parsnip Engines for Survival Models","title":"parsnip Engines for Survival Models","text":"censored parsnip extension package provides engines various models censored regression survival analysis.","code":""},{"path":"https://censored.tidymodels.org/dev/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"parsnip Engines for Survival Models","text":"can install released version censored CRAN : development version GitHub :","code":"install.packages(\"censored\") # install.packages(\"pak\") pak::pak(\"tidymodels/censored\")"},{"path":"https://censored.tidymodels.org/dev/index.html","id":"available-models-engines-and-prediction-types","dir":"","previous_headings":"","what":"Available models, engines, and prediction types","title":"parsnip Engines for Survival Models","text":"censored provides engines models following table. examples, please see Fitting Predicting censored. time event can predicted type = \"time\", survival probability type = \"survival\", linear predictor type = \"linear_pred\", quantiles event time distribution type = \"quantile\", hazard type = \"hazard\".","code":""},{"path":"https://censored.tidymodels.org/dev/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"parsnip Engines for Survival Models","text":"project released Contributor Code Conduct. contributing project, agree abide terms. questions discussions tidymodels packages, modeling, machine learning, please post RStudio Community. think encountered bug, please submit issue. Either way, learn create share reprex (minimal, reproducible example), clearly communicate code. Check details contributing guidelines tidymodels packages get help.","code":""},{"path":"https://censored.tidymodels.org/dev/reference/aorsf_internal.html","id":null,"dir":"Reference","previous_headings":"","what":"Internal helper function for aorsf objects — aorsf_internal","title":"Internal helper function for aorsf objects — aorsf_internal","text":"Internal helper function aorsf objects","code":""},{"path":"https://censored.tidymodels.org/dev/reference/aorsf_internal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Internal helper function for aorsf objects — aorsf_internal","text":"","code":"survival_prob_orsf(object, new_data, eval_time, time = deprecated())"},{"path":"https://censored.tidymodels.org/dev/reference/aorsf_internal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Internal helper function for aorsf objects — aorsf_internal","text":"object model object aorsf::orsf(). new_data data frame predicted. eval_time vector times predict survival probability. time Deprecated favor eval_time. vector times predict survival probability.","code":""},{"path":"https://censored.tidymodels.org/dev/reference/aorsf_internal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Internal helper function for aorsf objects — aorsf_internal","text":"tibble list column nested tibbles.","code":""},{"path":"https://censored.tidymodels.org/dev/reference/aorsf_internal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Internal helper function for aorsf objects — aorsf_internal","text":"","code":"library(aorsf) aorsf <- orsf(na.omit(lung), Surv(time, status) ~ age + ph.ecog, n_tree = 10) preds <- survival_prob_orsf(aorsf, lung[1:3, ], eval_time = c(250, 100))"},{"path":"https://censored.tidymodels.org/dev/reference/blackboost_train.html","id":null,"dir":"Reference","previous_headings":"","what":"Boosted trees via mboost — blackboost_train","title":"Boosted trees via mboost — blackboost_train","text":"blackboost_train() wrapper blackboost() function mboost package fits tree-based models model arguments main function.","code":""},{"path":"https://censored.tidymodels.org/dev/reference/blackboost_train.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Boosted trees via mboost — blackboost_train","text":"","code":"blackboost_train( formula, data, family, weights = NULL, teststat = \"quad\", testtype = \"Teststatistic\", mincriterion = 0, minsplit = 10, minbucket = 4, maxdepth = 2, saveinfo = FALSE, ... )"},{"path":"https://censored.tidymodels.org/dev/reference/blackboost_train.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Boosted trees via mboost — blackboost_train","text":"teststat character specifying type test statistic applied variable selection. testtype character specifying compute distribution test statistic. first three options refer p-values criterion, Teststatistic uses raw statistic criterion. Bonferroni Univariate relate p-values asymptotic distribution (adjusted unadjusted). Bonferroni-adjusted Monte-Carlo p-values computed Bonferroni MonteCarlo given. mincriterion value test statistic 1 - p-value must exceeded order implement split. minsplit minimum sum weights node order considered splitting. minbucket minimum sum weights terminal node. maxdepth maximum depth tree. default maxdepth = Inf means restrictions applied tree sizes. saveinfo logical. Store information variable selection procedure info slot partynode. ... arguments pass. x data frame matrix predictors. y factor vector 2 levels","code":""},{"path":"https://censored.tidymodels.org/dev/reference/blackboost_train.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Boosted trees via mboost — blackboost_train","text":"fitted blackboost model.","code":""},{"path":"https://censored.tidymodels.org/dev/reference/blackboost_train.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Boosted trees via mboost — blackboost_train","text":"","code":"blackboost_train(Surv(time, status) ~ age + ph.ecog, data = lung[-14, ], family = mboost::CoxPH() ) #> #> \t Model-based Boosting #> #> Call: #> mboost::blackboost(formula = formula, data = data, family = family, control = mboost::boost_control(), tree_controls = partykit::ctree_control(teststat = \"quad\", testtype = \"Teststatistic\", mincriterion = 0, minsplit = 10, minbucket = 4, maxdepth = 2, saveinfo = FALSE)) #> #> #> \t Cox Partial Likelihood #> #> Loss function: #> #> Number of boosting iterations: mstop = 100 #> Step size: 0.1 #> Offset: 0 #> Number of baselearners: 1 #>"},{"path":"https://censored.tidymodels.org/dev/reference/censored-package.html","id":null,"dir":"Reference","previous_headings":"","what":"censored: parsnip Engines for Survival Models — censored-package","title":"censored: parsnip Engines for Survival Models — censored-package","text":"censored provides engines survival models parsnip package. models include parametric survival models, proportional hazards models, decision trees, boosted trees, bagged trees, random forests. See \"Fitting Predicting censored\" article various examples. See examples classic survival models fit censored.","code":""},{"path":[]},{"path":"https://censored.tidymodels.org/dev/reference/censored-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"censored: parsnip Engines for Survival Models — censored-package","text":"Maintainer: Hannah Frick hannah@posit.co (ORCID) Authors: Emil Hvitfeldt emil.hvitfeldt@posit.co (ORCID) contributors: Posit Software, PBC [copyright holder, funder]","code":""},{"path":"https://censored.tidymodels.org/dev/reference/censored-package.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"censored: parsnip Engines for Survival Models — censored-package","text":"","code":"# Accelerated Failure Time (AFT) model fit_aft <- survival_reg(dist = \"weibull\") %>% set_engine(\"survival\") %>% fit(Surv(time, status) ~ age + sex + ph.karno, data = lung) predict(fit_aft, lung[1:3, ], type = \"time\") #> # A tibble: 3 × 1 #> .pred_time #> #> 1 355. #> 2 374. #> 3 416. # Cox's Proportional Hazards model fit_cox <- proportional_hazards() %>% set_engine(\"survival\") %>% fit(Surv(time, status) ~ age + sex + ph.karno, data = lung) predict(fit_cox, lung[1:3, ], type = \"time\") #> # A tibble: 3 × 1 #> .pred_time #> #> 1 325. #> 2 343. #> 3 379. # Andersen-Gill model for recurring events fit_ag <- proportional_hazards() %>% set_engine(\"survival\") %>% fit(Surv(tstart, tstop, status) ~ treat + inherit + age + strata(hos.cat), data = cgd ) predict(fit_ag, cgd[1:3, ], type = \"time\") #> # A tibble: 3 × 1 #> .pred_time #> #> 1 319. #> 2 319. #> 3 319."},{"path":"https://censored.tidymodels.org/dev/reference/coxnet_train.html","id":null,"dir":"Reference","previous_headings":"","what":"Wrapper for glmnet for censored — coxnet_train","title":"Wrapper for glmnet for censored — coxnet_train","text":"used directly users.","code":""},{"path":"https://censored.tidymodels.org/dev/reference/coxnet_train.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Wrapper for glmnet for censored — coxnet_train","text":"","code":"coxnet_train( formula, data, alpha = 1, lambda = NULL, weights = NULL, ..., call = caller_env() )"},{"path":"https://censored.tidymodels.org/dev/reference/coxnet_train.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Wrapper for glmnet for censored — coxnet_train","text":"formula model formula. data data. alpha elasticnet mixing parameter, \\(0\\le\\alpha\\le 1\\). penalty defined $$(1-\\alpha)/2||\\beta||_2^2+\\alpha||\\beta||_1.$$ alpha=1 lasso penalty, alpha=0 ridge penalty. lambda user supplied lambda sequence. Typical usage program compute lambda sequence based nlambda lambda.min.ratio. Supplying value lambda overrides . WARNING: use care. Avoid supplying single value lambda (predictions CV use predict() instead). Supply instead decreasing sequence lambda values. glmnet relies warms starts speed, often faster fit whole path compute single fit. weights observation weights. Can total counts responses proportion matrices. Default 1 observation ... additional parameters passed glmnet::glmnet. call call passed rlang::abort().","code":""},{"path":"https://censored.tidymodels.org/dev/reference/coxnet_train.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Wrapper for glmnet for censored — coxnet_train","text":"fitted glmnet model.","code":""},{"path":"https://censored.tidymodels.org/dev/reference/coxnet_train.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Wrapper for glmnet for censored — coxnet_train","text":"wrapper translates formula interface glmnet's matrix due stratification can specified. glmnet requires response stratified via glmnet::stratifySurv(). censored allows specification via survival::strata() term right-hand side formula. formula used generate stratification information needed stratifying response. formula without strata term used generating model matrix glmnet. wrapper retains original formula pre-processing elements including training data allow predictions fitted model.","code":""},{"path":"https://censored.tidymodels.org/dev/reference/coxnet_train.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Wrapper for glmnet for censored — coxnet_train","text":"","code":"coxnet_mod <- coxnet_train(Surv(time, status) ~ age + sex, data = lung)"},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_coxnet.html","id":null,"dir":"Reference","previous_headings":"","what":"A wrapper for survival probabilities with coxnet models — survival_prob_coxnet","title":"A wrapper for survival probabilities with coxnet models — survival_prob_coxnet","text":"wrapper survival probabilities coxnet models","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_coxnet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A wrapper for survival probabilities with coxnet models — survival_prob_coxnet","text":"","code":"survival_prob_coxnet( object, new_data, eval_time, time = deprecated(), output = \"surv\", penalty = NULL, ... )"},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_coxnet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A wrapper for survival probabilities with coxnet models — survival_prob_coxnet","text":"object fitted _coxnet object. new_data Data prediction. eval_time vector integers prediction times. time Deprecated favor eval_time. vector integers prediction times. output One \"surv\" \"haz\". penalty Penalty value(s). ... Options pass survival::survfit().","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_coxnet.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A wrapper for survival probabilities with coxnet models — survival_prob_coxnet","text":"tibble list column nested tibbles.","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_coxnet.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A wrapper for survival probabilities with coxnet models — survival_prob_coxnet","text":"","code":"cox_mod <- proportional_hazards(penalty = 0.1) %>% set_engine(\"glmnet\") %>% fit(Surv(time, status) ~ ., data = lung) survival_prob_coxnet(cox_mod, new_data = lung[1:3, ], eval_time = 300) #> # A tibble: 3 × 1 #> .pred #> #> 1 #> 2 #> 3 "},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_coxph.html","id":null,"dir":"Reference","previous_headings":"","what":"A wrapper for survival probabilities with coxph models — survival_prob_coxph","title":"A wrapper for survival probabilities with coxph models — survival_prob_coxph","text":"wrapper survival probabilities coxph models","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_coxph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A wrapper for survival probabilities with coxph models — survival_prob_coxph","text":"","code":"survival_prob_coxph( x, new_data, eval_time, time = deprecated(), output = \"surv\", interval = \"none\", conf.int = 0.95, ... )"},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_coxph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A wrapper for survival probabilities with coxph models — survival_prob_coxph","text":"x model coxph(). new_data Data prediction eval_time vector integers prediction times. time Deprecated favor eval_time. vector integers prediction times. output One \"surv\", \"conf\", \"haz\". interval Add confidence interval survival probability? Options \"none\" \"confidence\". conf.int confidence level. ... Options pass survival::survfit()","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_coxph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A wrapper for survival probabilities with coxph models — survival_prob_coxph","text":"tibble list column nested tibbles.","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_coxph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A wrapper for survival probabilities with coxph models — survival_prob_coxph","text":"","code":"cox_mod <- coxph(Surv(time, status) ~ ., data = lung) survival_prob_coxph(cox_mod, new_data = lung[1:3, ], eval_time = 300) #> # A tibble: 3 × 1 #> .pred #> #> 1 #> 2 #> 3 "},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_mboost.html","id":null,"dir":"Reference","previous_headings":"","what":"A wrapper for survival probabilities with mboost models — survival_prob_mboost","title":"A wrapper for survival probabilities with mboost models — survival_prob_mboost","text":"wrapper survival probabilities mboost models","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_mboost.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A wrapper for survival probabilities with mboost models — survival_prob_mboost","text":"","code":"survival_prob_mboost(object, new_data, eval_time, time = deprecated())"},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_mboost.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A wrapper for survival probabilities with mboost models — survival_prob_mboost","text":"new_data Data prediction. eval_time vector integers prediction times. time Deprecated favor eval_time. vector integers prediction times. x model blackboost().","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_mboost.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A wrapper for survival probabilities with mboost models — survival_prob_mboost","text":"tibble list column nested tibbles.","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_mboost.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A wrapper for survival probabilities with mboost models — survival_prob_mboost","text":"","code":"library(mboost) #> Loading required package: parallel #> Loading required package: stabs mod <- blackboost(Surv(time, status) ~ ., data = lung, family = CoxPH()) survival_prob_mboost(mod, new_data = lung[1:3, ], eval_time = 300) #> # A tibble: 3 × 1 #> .pred #> #> 1 #> 2 #> 3 "},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_partykit.html","id":null,"dir":"Reference","previous_headings":"","what":"A wrapper for survival probabilities with partykit models — survival_prob_partykit","title":"A wrapper for survival probabilities with partykit models — survival_prob_partykit","text":"wrapper survival probabilities partykit models","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_partykit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A wrapper for survival probabilities with partykit models — survival_prob_partykit","text":"","code":"survival_prob_partykit( object, new_data, eval_time, time = deprecated(), output = \"surv\" )"},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_partykit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A wrapper for survival probabilities with partykit models — survival_prob_partykit","text":"object model object partykit::ctree() partykit::cforest(). new_data data frame predicted. eval_time vector times predict survival probability. time Deprecated favor eval_time. vector times predict survival probability. output Type output. Can either \"surv\" \"haz\".","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_partykit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A wrapper for survival probabilities with partykit models — survival_prob_partykit","text":"tibble list column nested tibbles.","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_partykit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A wrapper for survival probabilities with partykit models — survival_prob_partykit","text":"","code":"library(partykit) #> Loading required package: grid #> Loading required package: libcoin #> Loading required package: mvtnorm #> #> Attaching package: ‘partykit’ #> The following object is masked from ‘package:mboost’: #> #> varimp c_tree <- ctree(Surv(time, status) ~ age + ph.ecog, data = lung) survival_prob_partykit(c_tree, lung[1:3, ], eval_time = 100) #> # A tibble: 3 × 1 #> .pred #> #> 1 #> 2 #> 3 c_forest <- cforest(Surv(time, status) ~ age + ph.ecog, data = lung, ntree = 10) survival_prob_partykit(c_forest, lung[1:3, ], eval_time = 100) #> # A tibble: 3 × 1 #> .pred #> #> 1 #> 2 #> 3 "},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_survbagg.html","id":null,"dir":"Reference","previous_headings":"","what":"A wrapper for survival probabilities with survbagg models — survival_prob_survbagg","title":"A wrapper for survival probabilities with survbagg models — survival_prob_survbagg","text":"wrapper survival probabilities survbagg models","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_survbagg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A wrapper for survival probabilities with survbagg models — survival_prob_survbagg","text":"","code":"survival_prob_survbagg(object, new_data, eval_time, time = deprecated())"},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_survbagg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A wrapper for survival probabilities with survbagg models — survival_prob_survbagg","text":"object model ipred::bagging(). new_data Data prediction. eval_time vector prediction times. time Deprecated favor eval_time. vector prediction times.","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_survbagg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A wrapper for survival probabilities with survbagg models — survival_prob_survbagg","text":"vctrs list tibbles.","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_survbagg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A wrapper for survival probabilities with survbagg models — survival_prob_survbagg","text":"","code":"library(ipred) #> #> Attaching package: ‘ipred’ #> The following object is masked from ‘package:mboost’: #> #> cv bagged_tree <- bagging(Surv(time, status) ~ age + ph.ecog, data = lung) survival_prob_survbagg(bagged_tree, lung[1:3, ], eval_time = 100) #> # A tibble: 3 × 1 #> .pred #> #> 1 #> 2 #> 3 "},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_survreg.html","id":null,"dir":"Reference","previous_headings":"","what":"Internal function helps for parametric survival models — survival_prob_survreg","title":"Internal function helps for parametric survival models — survival_prob_survreg","text":"Internal function helps parametric survival models","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_survreg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Internal function helps for parametric survival models — survival_prob_survreg","text":"","code":"survival_prob_survreg(object, new_data, eval_time, time = deprecated()) hazard_survreg(object, new_data, eval_time)"},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_survreg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Internal function helps for parametric survival models — survival_prob_survreg","text":"object survreg object. new_data data frame. eval_time vector time points. time Deprecated favor eval_time. vector time points.","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_survreg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Internal function helps for parametric survival models — survival_prob_survreg","text":"tibble list column nested tibbles.","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_prob_survreg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Internal function helps for parametric survival models — survival_prob_survreg","text":"","code":"surv_reg <- survreg(Surv(time, status) ~ ., data = lung) survival_prob_survreg(surv_reg, lung[1:3, ], eval_time = 100) #> # A tibble: 3 × 1 #> .pred #> #> 1 #> 2 #> 3 hazard_survreg(surv_reg, lung[1:3, ], eval_time = 100) #> # A tibble: 3 × 1 #> .pred #> #> 1 #> 2 #> 3 "},{"path":"https://censored.tidymodels.org/dev/reference/survival_time_coxnet.html","id":null,"dir":"Reference","previous_headings":"","what":"A wrapper for survival times with coxnet models — survival_time_coxnet","title":"A wrapper for survival times with coxnet models — survival_time_coxnet","text":"wrapper survival times coxnet models","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_time_coxnet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A wrapper for survival times with coxnet models — survival_time_coxnet","text":"","code":"survival_time_coxnet(object, new_data, penalty = NULL, multi = FALSE, ...)"},{"path":"https://censored.tidymodels.org/dev/reference/survival_time_coxnet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A wrapper for survival times with coxnet models — survival_time_coxnet","text":"object fitted _coxnet object. new_data Data prediction. penalty Penalty value(s). multi Allow multiple penalty values? ... Options pass survival::survfit().","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_time_coxnet.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A wrapper for survival times with coxnet models — survival_time_coxnet","text":"vector.","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_time_coxnet.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A wrapper for survival times with coxnet models — survival_time_coxnet","text":"","code":"cox_mod <- proportional_hazards(penalty = 0.1) %>% set_engine(\"glmnet\") %>% fit(Surv(time, status) ~ ., data = lung) survival_time_coxnet(cox_mod, new_data = lung[1:3, ], penalty = 0.1) #> [1] NA 425.4722 NA"},{"path":"https://censored.tidymodels.org/dev/reference/survival_time_coxph.html","id":null,"dir":"Reference","previous_headings":"","what":"A wrapper for survival times with coxph models — survival_time_coxph","title":"A wrapper for survival times with coxph models — survival_time_coxph","text":"wrapper survival times coxph models","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_time_coxph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A wrapper for survival times with coxph models — survival_time_coxph","text":"","code":"survival_time_coxph(object, new_data)"},{"path":"https://censored.tidymodels.org/dev/reference/survival_time_coxph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A wrapper for survival times with coxph models — survival_time_coxph","text":"object model coxph(). new_data Data prediction","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_time_coxph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A wrapper for survival times with coxph models — survival_time_coxph","text":"vector.","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_time_coxph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A wrapper for survival times with coxph models — survival_time_coxph","text":"","code":"cox_mod <- coxph(Surv(time, status) ~ ., data = lung) survival_time_coxph(cox_mod, new_data = lung[1:3, ]) #> [1] NA 470.5813 NA"},{"path":"https://censored.tidymodels.org/dev/reference/survival_time_mboost.html","id":null,"dir":"Reference","previous_headings":"","what":"A wrapper for mean survival times with mboost models — survival_time_mboost","title":"A wrapper for mean survival times with mboost models — survival_time_mboost","text":"wrapper mean survival times mboost models","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_time_mboost.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A wrapper for mean survival times with mboost models — survival_time_mboost","text":"","code":"survival_time_mboost(object, new_data)"},{"path":"https://censored.tidymodels.org/dev/reference/survival_time_mboost.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A wrapper for mean survival times with mboost models — survival_time_mboost","text":"object model blackboost(). new_data Data prediction","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_time_mboost.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A wrapper for mean survival times with mboost models — survival_time_mboost","text":"tibble.","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_time_mboost.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A wrapper for mean survival times with mboost models — survival_time_mboost","text":"","code":"library(mboost) boosted_tree <- blackboost(Surv(time, status) ~ age + ph.ecog, data = lung[-14, ], family = CoxPH() ) survival_time_mboost(boosted_tree, new_data = lung[1:3, ]) #> # A tibble: 3 × 1 #> .pred_time #> #> 1 370. #> 2 337. #> 3 540."},{"path":"https://censored.tidymodels.org/dev/reference/survival_time_survbagg.html","id":null,"dir":"Reference","previous_headings":"","what":"A wrapper for survival times with survbagg models — survival_time_survbagg","title":"A wrapper for survival times with survbagg models — survival_time_survbagg","text":"wrapper survival times survbagg models","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_time_survbagg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A wrapper for survival times with survbagg models — survival_time_survbagg","text":"","code":"survival_time_survbagg(object, new_data)"},{"path":"https://censored.tidymodels.org/dev/reference/survival_time_survbagg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A wrapper for survival times with survbagg models — survival_time_survbagg","text":"object model ipred::bagging(). new_data Data prediction","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_time_survbagg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A wrapper for survival times with survbagg models — survival_time_survbagg","text":"vector.","code":""},{"path":"https://censored.tidymodels.org/dev/reference/survival_time_survbagg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A wrapper for survival times with survbagg models — survival_time_survbagg","text":"","code":"library(ipred) bagged_tree <- bagging(Surv(time, status) ~ age + ph.ecog, data = lung) survival_time_survbagg(bagged_tree, lung[1:3, ]) #> [1] 310 350 450"},{"path":"https://censored.tidymodels.org/dev/reference/time_to_million.html","id":null,"dir":"Reference","previous_headings":"","what":"Number of days before a movie grosses $1M USD — time_to_million","title":"Number of days before a movie grosses $1M USD — time_to_million","text":"data somewhat biased random sample 551 movies released 2015 2018. Columns include","code":""},{"path":"https://censored.tidymodels.org/dev/reference/time_to_million.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Number of days before a movie grosses $1M USD — time_to_million","text":"time_to_million tibble","code":""},{"path":"https://censored.tidymodels.org/dev/reference/time_to_million.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Number of days before a movie grosses $1M USD — time_to_million","text":"title: character string movie title. time: number days movie earns million US dollars. event: binary value whether movie reached goal. 94% movies observed events. released: date field release date. distributor: factor name distributor. released_theaters: maximum number theaters movie played first two weeks release. year: release year. rated: factor Motion Picture Association film rating. runtime: length movie (minutes). set indicators columns movie genre (e.g. action, crime, etc.). set indicators language (e.g., english, hindi, etc.). set indicators countries movie released (e.g., uk, japan, etc.)","code":""},{"path":"https://censored.tidymodels.org/dev/news/index.html","id":"censored-development-version","dir":"Changelog","previous_headings":"","what":"censored (development version)","title":"censored (development version)","text":"Fixed bug proportional_hazards(engine = \"glmnet\") prediction didn’t work workflow() formula preprocessor (#264). extract_fit_engine() now works properly proportional hazards models fitted \"glmnet\" engine (#266). survival_time_coxnet() gained multi argument allow multiple values penalty (#278).","code":""},{"path":"https://censored.tidymodels.org/dev/news/index.html","id":"censored-020","dir":"Changelog","previous_headings":"","what":"censored 0.2.0","title":"censored 0.2.0","text":"CRAN release: 2023-04-13","code":""},{"path":"https://censored.tidymodels.org/dev/news/index.html","id":"cross-package-changes-with-parsnip-0-2-0","dir":"Changelog","previous_headings":"","what":"Cross-package changes with parsnip","title":"censored 0.2.0","text":"new eval_time argument replaces time argument time points predict survival probability hazard. time argument deprecated (#244). matrix interface fitting, fit_xy(), now works censored regression models (#225, #234, #247, #251). Improved error messages throughout package (#248).","code":""},{"path":"https://censored.tidymodels.org/dev/news/index.html","id":"new-engines-0-2-0","dir":"Changelog","previous_headings":"","what":"New engines","title":"censored 0.2.0","text":"Added new \"aorsf\" engine rand_forest() accelerated oblique random survival forests aorsf package (@bcjaeger, #211). Added new flexsurvspline engine survival_reg() (@mattwarkentin, #213).","code":""},{"path":"https://censored.tidymodels.org/dev/news/index.html","id":"bug-fixes-0-2-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"censored 0.2.0","text":"Predictions type \"linear_pred\" survival_reg(engine = \"flexsurv\") now correct scale distributions natural scale unrestricted scale location parameter identical, e.g. dist = \"lnorm\" (#229). Predictions type \"linear_pred\" proportional_hazards(engine = \"glmnet\") via multi_predict() now sign via predict() (#242). Predictions survival probability survival_reg(engine = \"flexsurv\") single time point now nested correctly (#254). Predictions survival probability decision_tree(engine = \"rpart\") single observation now work (#256). Predictions type \"quantile\" survival_reg(engine = \"survival\") single observation now work (#257). Fixed bug printing coxnet models, .e., proportional_hazards() models fitted \"glmnet\" engine (#249).","code":""},{"path":"https://censored.tidymodels.org/dev/news/index.html","id":"internal-changes-0-2-0","dir":"Changelog","previous_headings":"","what":"Internal changes","title":"censored 0.2.0","text":"Predictions survival probabilities now calculated via summary.survfit() proportional_hazards() models \"survival\" \"glmnet\" engines, bag_tree() models \"rpart\" engine, decision_tree() models \"partykit\" engines, well rand_forest() models \"partykit\" engine (#221, #224). Added internal survfit_summary_*() helper functions (#216).","code":""},{"path":"https://censored.tidymodels.org/dev/news/index.html","id":"censored-011","dir":"Changelog","previous_headings":"","what":"censored 0.1.1","title":"censored 0.1.1","text":"CRAN release: 2022-09-30 boosted trees \"mboost\" engine, survival probabilities can now predicted time = -Inf. always 1. time = Inf now predicts survival probability 0 (#215). Updated tests model arguments update() methods (#208). Internal re-organisation code (#206, 209). Added NEWS.md file track changes package.","code":""}]