From 26a5d83b5c19416679cf27356a3f1cb294081bcd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CErika?= Date: Wed, 24 Apr 2024 11:41:32 -0600 Subject: [PATCH 01/16] edits from 4/24 class --- 2_modelling_flow.Rmd | 102 +++++++++++++++++++++++++++++++------------ 1 file changed, 75 insertions(+), 27 deletions(-) diff --git a/2_modelling_flow.Rmd b/2_modelling_flow.Rmd index 1942f55..281ef74 100644 --- a/2_modelling_flow.Rmd +++ b/2_modelling_flow.Rmd @@ -3,6 +3,9 @@ title: "Modelling Flow" author: "Matthew Ross" date: "2024-04-24" output: html_document +editor_options: + markdown: + wrap: 72 --- ```{r setup, include=FALSE} @@ -13,57 +16,102 @@ knitr::opts_chunk$set(echo = TRUE) # Modelling Flow -Now that we have explored individual correlations between long-term flow +Now that we have explored individual correlations between long-term flow characteristics and potential drivers of those characteristics (climate, -soils, etc...), we can start to build out increasingly complex models -to predict flow characteristics. +soils, etc...), we can start to build out increasingly complex models to +predict flow characteristics. # Assignment - ## Build a parsimonious linear model -Pick one of the flow characteristics that mosts interests you and use the `lm` -function to build a parsimonious model that predicts your favorite characteristic. What is parsimony? It's a model that is complex enough to -capture the major controls, but simple enough to be understandable. You -will need to explore, read, understand the differences between a + sign interaction, a ":" interaction and a * interaction in `lm` terminology. +Pick one of the flow characteristics that mosts interests you and use +the `lm` function to build a parsimonious model that predicts your +favorite characteristic. What is parsimony? It's a model that is complex +enough to capture the major controls, but simple enough to be +understandable. You will need to explore, read, understand the +differences between a + sign interaction, a ":" interaction and a \* +interaction in `lm` terminology. -Please report your R2, slope estimates, and p-value of your model and -write out your interpretation of these numbers. +Please report your R2, slope estimates, and p-value of your model and +write out your interpretation of these numbers. ```{r} - +#plot simple relationship +ggplot(hydro, aes(x = slope_fdc, y = q95)) + + geom_point() + + geom_smooth(method = 'lm', se = F) + +png(filename = 'bighydroplot.png', width = 10, height = 8, units = 'in', res = 300) +hydro %>% + select_if(is.numeric) %>% + ggpairs() +dev.off() + +#create simple model +slope_mod <- lm(q95 ~ slope_fdc, data = hydro) + +long_hydro <- hydro %>% + select_if(is.numeric) %>% + #selecting only the columns I want, had to do it this way because the order was weird + select(-c(q5, zero_q_freq, hfd_mean, high_q_freq, high_q_dur, low_q_freq, low_q_dur)) %>% + pivot_longer(cols = q_mean:stream_elas, + values_to = 'value', + names_to = 'driver') + +#plotting +ggplot(long_hydro, aes(value, + q95)) + + geom_point() + + facet_grid(~driver, + scales = 'free') + +# Get the summary of the model +summary(slope_mod) ``` +Answer: -## Build a CART model to predict flow. +slope estimates = 4.7278 -Linear models help us both predict and understand drivers of change, machine learning can help us understand drivers of change, but as a technique it is -more suited to accurate predictions. CART or Classification and Regression Trees -are a nice intermediate between lms and ml. Tons of resources for this but -[CART Logic](https://koalaverse.github.io/machine-learning-in-R/decision-trees.html#cart-software-in-r), provides a good conceptual overview, and [CART demo](https://www.statmethods.net/advstats/cart.html) provides a good enough code demo. +r-squared = 0.2361 -Read the logic intro above, and the code demo as well, to build a CART model -version of your lm. Use the code to visualize your CART output. +p-value = \< 2e-16 -```{r} +The slope controls \~24% of the variation in q95, where 1-unit increase, +q95 is predicted to increase by 4.73 mm. This is a modest linear +correlation. -``` +## Build a CART model to predict flow. +Linear models help us both predict and understand drivers of change, +machine learning can help us understand drivers of change, but as a +technique it is more suited to accurate predictions. CART or +Classification and Regression Trees are a nice intermediate between lms +and ml. Tons of resources for this but [CART +Logic](https://koalaverse.github.io/machine-learning-in-R/decision-trees.html#cart-software-in-r), +provides a good conceptual overview, and [CART +demo](https://www.statmethods.net/advstats/cart.html) provides a good +enough code demo. -## Build a RandomForest +Read the logic intro above, and the code demo as well, to build a CART +model version of your lm. Use the code to visualize your CART output. -CARTs are a single tree, what if we had thousands? Would we get better performance (yes!) +```{r} -The same CART logic site above introduces random forests as well. Please -read this part of the site and use the code demo to build your own RandomForest. -Remember, for a RandomForest type model we want to make sure we split our data -at least into train and test datasets and ideally into train-test-val. +``` +## Build a RandomForest +CARTs are a single tree, what if we had thousands? Would we get better +performance (yes!) +The same CART logic site above introduces random forests as well. Please +read this part of the site and use the code demo to build your own +RandomForest. Remember, for a RandomForest type model we want to make +sure we split our data at least into train and test datasets and ideally +into train-test-val. ```{r} ``` - From f6e86c83de8e7ac574c5b32f643080ec1badf5ea Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CErika?= Date: Wed, 24 Apr 2024 19:53:46 -0600 Subject: [PATCH 02/16] 4/24 finished assignment --- 2_modelling_flow.Rmd | 65 +++++++++++++++++++++----------------------- 1 file changed, 31 insertions(+), 34 deletions(-) diff --git a/2_modelling_flow.Rmd b/2_modelling_flow.Rmd index 281ef74..226c833 100644 --- a/2_modelling_flow.Rmd +++ b/2_modelling_flow.Rmd @@ -38,49 +38,29 @@ write out your interpretation of these numbers. ```{r} #plot simple relationship -ggplot(hydro, aes(x = slope_fdc, y = q95)) + - geom_point() + - geom_smooth(method = 'lm', se = F) - -png(filename = 'bighydroplot.png', width = 10, height = 8, units = 'in', res = 300) -hydro %>% - select_if(is.numeric) %>% - ggpairs() -dev.off() - -#create simple model -slope_mod <- lm(q95 ~ slope_fdc, data = hydro) - -long_hydro <- hydro %>% - select_if(is.numeric) %>% - #selecting only the columns I want, had to do it this way because the order was weird - select(-c(q5, zero_q_freq, hfd_mean, high_q_freq, high_q_dur, low_q_freq, low_q_dur)) %>% - pivot_longer(cols = q_mean:stream_elas, - values_to = 'value', - names_to = 'driver') - -#plotting -ggplot(long_hydro, aes(value, - q95)) + - geom_point() + - facet_grid(~driver, - scales = 'free') +#create parsimonious model +slope_mod <- lm(q_mean ~ slope_fdc * runoff_ratio * baseflow_index, data = hydro) # Get the summary of the model summary(slope_mod) ``` -Answer: +**Answer:** -slope estimates = 4.7278 +Slope Estimate: -r-squared = 0.2361 +- A one-unit increase in **`slope_fdc`** is associated with a decrease + of \~0.5330 units in **`q_mean`** -p-value = \< 2e-16 +- A one-unit increase in **`runoff_ratio`** is associated with an + increase of \~0.9667 units in **`q_mean`**. + +- A one-unit increase in **`baseflow_index`** is associated with a + decrease of \~0.3779 units in **`q_mean`** -The slope controls \~24% of the variation in q95, where 1-unit increase, -q95 is predicted to increase by 4.73 mm. This is a modest linear -correlation. +r-squared = 0.8056 + +p-value = \< 2e-16 ## Build a CART model to predict flow. @@ -98,7 +78,24 @@ Read the logic intro above, and the code demo as well, to build a CART model version of your lm. Use the code to visualize your CART output. ```{r} +library(rpart) + +cart_mod <- rpart(q_mean ~ slope_fdc+runoff_ratio+baseflow_index, data=hydro, method = "anova") + +# visualize cross-validation results +plotcp(cart_mod) + +#summarize data +summary(cart_mod) + +# create additional plots +rsq.rpart(cart_mod) # visualize cross-validation results +# plot tree +plot(cart_mod, uniform=TRUE, + main="Regression Tree for Mean Flow ") +text(cart_mod, use.n=TRUE, all=TRUE, cex=.8) +##not sure that this shows much information... did I do it wrong? ``` ## Build a RandomForest From 9b7f5a227493393bbf751dbae6c9152f22274d80 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CErika?= Date: Wed, 24 Apr 2024 19:53:59 -0600 Subject: [PATCH 03/16] V2, not correct version --- 2_modelling_flow_V2.Rmd | 127 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 127 insertions(+) create mode 100644 2_modelling_flow_V2.Rmd diff --git a/2_modelling_flow_V2.Rmd b/2_modelling_flow_V2.Rmd new file mode 100644 index 0000000..3402f8a --- /dev/null +++ b/2_modelling_flow_V2.Rmd @@ -0,0 +1,127 @@ +--- +title: "Modelling Flow" +author: "Matthew Ross" +date: "2024-04-24" +output: html_document +editor_options: + markdown: + wrap: 72 +--- + +```{r setup, include=FALSE} + +library(tidyverse) +library(rpart) +knitr::opts_chunk$set(echo = TRUE) +``` + +# Modelling Flow + +Now that we have explored individual correlations between long-term flow +characteristics and potential drivers of those characteristics (climate, +soils, etc...), we can start to build out increasingly complex models to +predict flow characteristics. + +# Assignment + +## Build a parsimonious linear model + +Pick one of the flow characteristics that mosts interests you and use +the `lm` function to build a parsimonious model that predicts your +favorite characteristic. What is parsimony? It's a model that is complex +enough to capture the major controls, but simple enough to be +understandable. You will need to explore, read, understand the +differences between a + sign interaction, a ":" interaction and a \* +interaction in `lm` terminology. + +Please report your R2, slope estimates, and p-value of your model and +write out your interpretation of these numbers. + +```{r} + +#create parsimonious model +slope_mod <- lm(q_mean ~ slope_fdc * runoff_ratio * baseflow_index, data = hydro) + +#I don't think this is the correct way to plot the parsimonious function +long_hydro <- hydro %>% + select_if(is.numeric) %>% + #selecting only the columns I want, had to do it this way because the order was weird + select(-c(q5, zero_q_freq, hfd_mean, high_q_freq, high_q_dur, low_q_freq, low_q_dur)) %>% + pivot_longer(cols = q_mean:stream_elas, + values_to = 'value', + names_to = 'driver') + +#plotting +ggplot(long_hydro, aes(value, + q95)) + + geom_point() + + facet_grid(~driver, + scales = 'free') + +# Get the summary of the model +summary(slope_mod) +``` + +```{r} +##parsimonious model +#predict values using the model +hydro_predicted <- hydro %>% + mutate(q_mean_predicted = predict(slope_mod, newdata = .)) + +#combine predicted value with original values +hydro_combined <- bind_cols(hydro, hydro_predicted) + +#plot - not working due to naming issues of variables +ggplot(hydro, aes(x = slope_fdc, y = q_mean)) + + geom_point() + + geom_smooth(method = "lm", formula = y ~ x * runoff_ratio * baseflow_index, se = FALSE, color = "red") + + labs(title = "Parsimonious Linear Model", + x = "Slope FDC", + y = "Q Mean") +``` + +Answer: + +slope estimates = 4.7278 + +r-squared = 0.2361 + +p-value = \< 2e-16 + +The slope controls \~24% of the variation in q95, where 1-unit increase, +q95 is predicted to increase by 4.73 mm. This is a modest linear +correlation. + +## Build a CART model to predict flow. + +Linear models help us both predict and understand drivers of change, +machine learning can help us understand drivers of change, but as a +technique it is more suited to accurate predictions. CART or +Classification and Regression Trees are a nice intermediate between lms +and ml. Tons of resources for this but [CART +Logic](https://koalaverse.github.io/machine-learning-in-R/decision-trees.html#cart-software-in-r), +provides a good conceptual overview, and [CART +demo](https://www.statmethods.net/advstats/cart.html) provides a good +enough code demo. + +Read the logic intro above, and the code demo as well, to build a CART +model version of your lm. Use the code to visualize your CART output. + +```{r} + +``` + +## Build a RandomForest + +CARTs are a single tree, what if we had thousands? Would we get better +performance (yes!) + +The same CART logic site above introduces random forests as well. Please +read this part of the site and use the code demo to build your own +RandomForest. Remember, for a RandomForest type model we want to make +sure we split our data at least into train and test datasets and ideally +into train-test-val. + +```{r} + +``` From d58675bb1ba91323b616f996ee9e98a07cb48342 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CErika?= Date: Thu, 25 Apr 2024 13:27:12 -0600 Subject: [PATCH 04/16] edits from 4/25 --- 1_Download_Visualize.Rmd | 80 +++++++++++++++++++++++++++------------- 1 file changed, 55 insertions(+), 25 deletions(-) diff --git a/1_Download_Visualize.Rmd b/1_Download_Visualize.Rmd index 1c79637..63b63da 100644 --- a/1_Download_Visualize.Rmd +++ b/1_Download_Visualize.Rmd @@ -12,13 +12,9 @@ library(GGally) knitr::opts_chunk$set(echo = TRUE) ``` - # Data Acquisition -For this assignment we are going to be playing with annually -aggregated metrics of USGS flow data from the [CAMELS](https://ral.ucar.edu/solutions/products/camels) dataset. This dataset -has sparked a revolution in machine learning in hydrology. - +For this assignment we are going to be playing with annually aggregated metrics of USGS flow data from the [CAMELS](https://ral.ucar.edu/solutions/products/camels) dataset. This dataset has sparked a revolution in machine learning in hydrology. ```{r} if(!file.exists('data')){ @@ -60,10 +56,8 @@ download.file('https://gdex.ucar.edu/dataset/camels/file/camels_attributes_v2.0. ``` - ## Data org - ```{r} dat_files <- list.files('data', full.names = T) @@ -73,55 +67,47 @@ dat_files <- list.files('data', climate <- read_delim(dat_files[1], delim = ';') hydro <- read_delim('data/hydro.txt', delim = ';') + ``` ## Initial data viz - -### Baseflow - +### Baseflow ```{r} ggplot(hydro, aes(x = baseflow_index, y = q95)) + geom_point() - - ``` - -Baseflow doesn't strongly control Q95 in a predictable way. - - +Baseflow doesn't strongly control Q95 in a predictable way. ### Climate controls - ```{r} cq <- inner_join(climate, hydro %>% select(gauge_id, q95)) - ggplot(cq, aes(x = p_mean, y = q95)) + geom_point() + geom_smooth(method = 'lm', se = F) p_mean_mod <- lm(q95 ~ p_mean, data = cq) - ``` - #### All at once ```{r} png(filename = 'bigclimeplot.png', width = 10, height = 8, units = 'in', res = 300) + cq %>% select_if(is.numeric) %>% ggpairs() + dev.off() @@ -138,17 +124,61 @@ ggplot(long_cq, aes(value, scales = 'free') ``` -The average precip (p_mean) controls 71% of the variation in 195, where every 1 mm/day increase in long-term average precip increases the q95 by 2.95 mm/day. - +The average precip (p_mean) controls 71% of the variation in 195, where every 1 mm/day increase in long-term average precip increases the q95 by 2.95 mm/day. # Assignment -## What are three controls on average runoff ratio? +## What are three controls on average runoff ratio? -## What are three controls on baseflow_index? +```{r} +cq_rr <- inner_join(climate, hydro %>% + select(gauge_id, runoff_ratio)) +``` -## What are three controls on mean flow? +```{r} +png(filename = 'bigrrplot.png', width = 10, height = 8, units = 'in', res = 300) +cq_rr %>% + select_if(is.numeric) %>% + ggpairs() +dev.off() +``` + +```{r} +#plot relationships +ggplot(cq_rr, aes(x=p_mean, y = runoff_ratio)) + geom_point() + geom_smooth(method = "lm", se = F) + +ggplot(cq_rr, aes(x=high_prec_freq, y = runoff_ratio)) + geom_point() + geom_smooth(method = "lm", se = F) + +ggplot(cq_rr, aes(x=low_prec_freq, y = runoff_ratio)) + geom_point() + geom_smooth(method = "lm", se = F) +``` + +```{r} +#model relationships to get summarys + +p_mean_rr_mod <- lm(runoff_ratio~p_mean, data = cq_rr) + +summary(p_mean_rr_mod) + +##repeat this for the other two! Then write a description of what we found +``` +Three controls on average runoff ratio include p_mean, high precipitation frequency, and low precipitation duration +## What are three controls on baseflow_index? + +```{r} +cq_bf <- inner_join(geol, hydro %>% + select(gauge_id, baseflow_index)) +``` + +```{r} + +``` + +## What are three controls on mean flow? + +```{r} + +``` From b65542302798efeff3ae268038ded9943b54b1e9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CErika?= Date: Thu, 25 Apr 2024 13:27:22 -0600 Subject: [PATCH 05/16] final rr plot --- bigrrplot.png | Bin 0 -> 961825 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 bigrrplot.png diff --git a/bigrrplot.png b/bigrrplot.png new file mode 100644 index 0000000000000000000000000000000000000000..f91f9f670d0e0ffd6fdd4b119142c3567683c465 GIT binary patch literal 961825 zcmeFZXH--DwmyoAC?aA*Ub>2kbd_F05e4a;NR3GEy@do+1XKi+7JBbhdWi%P0jZ&c z9(oB7I)p$%xeMRD&;Fk~?j7Tv_uC!gd`LzhS@k!|GoSg)^-)t@k@gbPB?<}(TBR4y 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zvbs6yV#@|dmvLc-Sx!9%wX6fTYv&F4Q|C&4@6YbQSUVT|?z%g@xOAWa#~Bb^b3e=DG`PnTLu=i$^(6XOdRvKEway?n_(1}v z^h)(py+@?EmbmL&>SC-ciDwR7hTa+MS?Lc=zgAO=SAtzgOo4qJgZ0(0x+i`c3#0aC zx|U;FJ^3)(PIelGX39*g8_3*O=>`;!7$YCxVJ5b^MDn+ANKA##A&sv-!; z1@G_tc}0DFf?${W@g=Rl+}{6xzuqf5M&M&GO1W4b|98va-v#x5`w9NLp#G2U8~-k- pe-{*R3;lP8`oBFD;K}<%${Af1twlObe+K*~C$0Rb@Zr Date: Thu, 25 Apr 2024 13:36:55 -0600 Subject: [PATCH 07/16] edits 4/25 --- 1_Download_Visualize.Rmd | 24 ++++++++++++++++++++++-- 1 file changed, 22 insertions(+), 2 deletions(-) diff --git a/1_Download_Visualize.Rmd b/1_Download_Visualize.Rmd index 63b63da..7ba102f 100644 --- a/1_Download_Visualize.Rmd +++ b/1_Download_Visualize.Rmd @@ -49,6 +49,10 @@ download.file('https://gdex.ucar.edu/dataset/camels/file/camels_vege.txt', download.file('https://gdex.ucar.edu/dataset/camels/file/camels_hydro.txt', 'data/hydro.txt') +#geol (response variable) +download.file('https://gdex.ucar.edu/dataset/camels/file/camels_geol.txt', + 'data/geol.txt') + # Variable definitions download.file('https://gdex.ucar.edu/dataset/camels/file/camels_attributes_v2.0.pdf', @@ -68,6 +72,7 @@ climate <- read_delim(dat_files[1], delim = ';') hydro <- read_delim('data/hydro.txt', delim = ';') +geol <- read_delim('data/geol.txt', delim = ';') ``` ## Initial data viz @@ -169,12 +174,27 @@ Three controls on average runoff ratio include p_mean, high precipitation freque ## What are three controls on baseflow_index? ```{r} -cq_bf <- inner_join(geol, hydro %>% - select(gauge_id, baseflow_index)) +cq_bf <- inner_join(climate,hydro %>% + select(gauge_id, baseflow_index)) ``` ```{r} +png(filename = 'bigbfplot.png', width = 10, height = 8, units = 'in', res = 300) + +cq_bf %>% + select_if(is.numeric) %>% + ggpairs() +dev.off() +``` + +```{r} +#plot relationships +ggplot(cq_bf, aes(x=p_mean, y = baseflow_index)) + geom_point() + geom_smooth(method = "lm", se = F) + +ggplot(cq_rr, aes(x=high_prec_freq, y = runoff_ratio)) + geom_point() + geom_smooth(method = "lm", se = F) + +ggplot(cq_rr, aes(x=low_prec_freq, y = runoff_ratio)) + geom_point() + geom_smooth(method = "lm", se = F) ``` ## What are three controls on mean flow? 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zvbs6yV#@|dmvLc-Sx!9%wX6fTYv&F4Q|C&4@6YbQSUVT|?z%g@xOAWa#~Bb^b3e=DG`PnTLu=i$^(6XOdRvKEway?n_(1}v z^h)(py+@?EmbmL&>SC-ciDwR7hTa+MS?Lc=zgAO=SAtzgOo4qJgZ0(0x+i`c3#0aC zx|U;FJ^3)(PIelGX39*g8_3*O=>`;!7$YCxVJ5b^MDn+ANKA##A&sv-!; z1@G_tc}0DFf?${W@g=Rl+}{6xzuqf5M&M&GO1W4b|98va-v#x5`w9NLp#G2U8~-k- pe-{*R3;lP8`oBFD;K}<%${Af1twlObe+K*~C$0Rb@Zr Date: Thu, 25 Apr 2024 20:02:46 -0600 Subject: [PATCH 09/16] completed download & visualize assignment --- 1_Download_Visualize.Rmd | 60 ++++++++++++++++++++++++++++++++++++---- 1 file changed, 55 insertions(+), 5 deletions(-) diff --git a/1_Download_Visualize.Rmd b/1_Download_Visualize.Rmd index 7ba102f..62237d2 100644 --- a/1_Download_Visualize.Rmd +++ b/1_Download_Visualize.Rmd @@ -163,13 +163,17 @@ ggplot(cq_rr, aes(x=low_prec_freq, y = runoff_ratio)) + geom_point() + geom_smoo #model relationships to get summarys p_mean_rr_mod <- lm(runoff_ratio~p_mean, data = cq_rr) - summary(p_mean_rr_mod) +high_p_rr_mod <- lm(runoff_ratio~high_prec_freq, data = cq_rr) +summary(high_p_rr_mod) + +low_p_rr_mod <- lm(runoff_ratio~low_prec_freq, data = cq_rr) +summary(low_p_rr_mod) ##repeat this for the other two! Then write a description of what we found ``` -Three controls on average runoff ratio include p_mean, high precipitation frequency, and low precipitation duration +Three controls on average runoff ratio include p_mean, high precipitation frequency, and low precipitation frequency. All three variables have a highly significant p-value in relationship to runoff ratio. ## What are three controls on baseflow_index? @@ -190,15 +194,61 @@ dev.off() ```{r} #plot relationships -ggplot(cq_bf, aes(x=p_mean, y = baseflow_index)) + geom_point() + geom_smooth(method = "lm", se = F) +ggplot(cq_bf, aes(x=frac_snow, y = baseflow_index)) + geom_point() + geom_smooth(method = "lm", se = F) -ggplot(cq_rr, aes(x=high_prec_freq, y = runoff_ratio)) + geom_point() + geom_smooth(method = "lm", se = F) +ggplot(cq_bf, aes(x=high_prec_freq, y = baseflow_index)) + geom_point() + geom_smooth(method = "lm", se = F) -ggplot(cq_rr, aes(x=low_prec_freq, y = runoff_ratio)) + geom_point() + geom_smooth(method = "lm", se = F) +ggplot(cq_bf, aes(x=low_prec_freq, y = baseflow_index)) + geom_point() + geom_smooth(method = "lm", se = F) +``` + +```{r} +frac_snow_bf_mod <- lm(baseflow_index~frac_snow, data = cq_bf) +summary(frac_snow_bf_mod) + +high_prec_freq_bf_mod <- lm(baseflow_index~high_prec_freq, data = cq_bf) +summary(high_prec_freq_bf_mod) + +low_prec_freq_bf_mod <- lm(baseflow_index~low_prec_freq, data = cq_bf) +summary(low_prec_freq_bf_mod) ``` +Three controls on average runoff ratio include fraction of precip falling as snow, high precipitation frequency, and low precipitation frequency. All three variables have a highly significant p-value in relationship to runoff ratio. + ## What are three controls on mean flow? ```{r} +cq_flow <- inner_join(climate,hydro %>% + select(gauge_id, q_mean)) +``` + +```{r} +png(filename = 'bigmeanflowplot.png', width = 10, height = 8, units = 'in', res = 300) +cq_flow %>% + select_if(is.numeric) %>% + ggpairs() + +dev.off() +``` + +```{r} +#plot relationships +ggplot(cq_flow, aes(x=p_mean, y = q_mean)) + geom_point() + geom_smooth(method = "lm", se = F) + +ggplot(cq_flow, aes(x=aridity, y = q_mean)) + geom_point() + geom_smooth(method = "lm", se = F) + +ggplot(cq_flow, aes(x=low_prec_dur, y = q_mean)) + geom_point() + geom_smooth(method = "lm", se = F) +``` + +```{r} +p_mean_q_mod <- lm(q_mean~p_mean, data = cq_flow) +summary(p_mean_q_mod) + +aridity_q_mod <- lm(q_mean~aridity, data = cq_flow) +summary(aridity_q_mod) + +low_prec_dur_q_mod <- lm(q_mean~low_prec_dur, data = cq_flow) +summary(low_prec_dur_q_mod) ``` + +Three controls on average runoff ratio include fraction of mean precipitation, aridity, and low precipitation duration. All three variables have a highly significant p-value in relationship to runoff ratio. 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zL$!C_-~H8ZcQpuH2^N%rmHO2ab&DR@!K=Ld_}>4rvHT2VEI*@d^n&$Qt=A_(hJyz^ zvm)OwKX}){m1x&f%SVNN)mYAT4%op@Uhuzj>Q}#=HFPCwHRlHXUwwq7?}7jhE;SZE zkhuGIY{mcTFYVBk+M{Ou3w|}K;QtHM|Lk7;e}Vd+owWb+1!`mW1|50e+S8O{{{Vkf M6gBQ( Date: Thu, 25 Apr 2024 20:03:15 -0600 Subject: [PATCH 11/16] completed modelling flow assingment --- 2_modelling_flow.Rmd | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/2_modelling_flow.Rmd b/2_modelling_flow.Rmd index 226c833..cfea6b6 100644 --- a/2_modelling_flow.Rmd +++ b/2_modelling_flow.Rmd @@ -11,6 +11,7 @@ editor_options: ```{r setup, include=FALSE} library(tidyverse) +library(rpart) knitr::opts_chunk$set(echo = TRUE) ``` @@ -39,10 +40,10 @@ write out your interpretation of these numbers. ```{r} #plot simple relationship #create parsimonious model -slope_mod <- lm(q_mean ~ slope_fdc * runoff_ratio * baseflow_index, data = hydro) +p_mod <- lm(q_mean ~ slope_fdc * runoff_ratio * baseflow_index, data = hydro) -# Get the summary of the model -summary(slope_mod) +#Get the summary of the model +summary(p_mod) ``` **Answer:** @@ -78,8 +79,6 @@ Read the logic intro above, and the code demo as well, to build a CART model version of your lm. Use the code to visualize your CART output. ```{r} -library(rpart) - cart_mod <- rpart(q_mean ~ slope_fdc+runoff_ratio+baseflow_index, data=hydro, method = "anova") # visualize cross-validation results @@ -94,8 +93,9 @@ rsq.rpart(cart_mod) # visualize cross-validation results # plot tree plot(cart_mod, uniform=TRUE, main="Regression Tree for Mean Flow ") -text(cart_mod, use.n=TRUE, all=TRUE, cex=.8) -##not sure that this shows much information... did I do it wrong? + +text(cart_mod, use.n=TRUE, all=TRUE, cex=.5) +##the titles for my text are not correct on this plot, bit sure why but I haven't been able to figure it out. ``` ## Build a RandomForest From 521603630ba1b3a93c2966042e269e2974186909 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CErika?= Date: Mon, 29 Apr 2024 11:18:41 -0600 Subject: [PATCH 12/16] unfinished assignment 2 --- 2_modelling_flow.Rmd | 141 +++++++++++++++++++++++++++++++++++++++---- 1 file changed, 128 insertions(+), 13 deletions(-) diff --git a/2_modelling_flow.Rmd b/2_modelling_flow.Rmd index cfea6b6..d17192a 100644 --- a/2_modelling_flow.Rmd +++ b/2_modelling_flow.Rmd @@ -12,6 +12,8 @@ editor_options: library(tidyverse) library(rpart) +library(ggplot2) + knitr::opts_chunk$set(echo = TRUE) ``` @@ -38,30 +40,100 @@ Please report your R2, slope estimates, and p-value of your model and write out your interpretation of these numbers. ```{r} +#create a dataframe with combining hydro and climate +chydro <- inner_join(hydro,climate) + #plot simple relationship #create parsimonious model -p_mod <- lm(q_mean ~ slope_fdc * runoff_ratio * baseflow_index, data = hydro) +q_mod <- lm(q_mean ~ runoff_ratio * p_mean, data = chydro) #Get the summary of the model -summary(p_mod) +summary(q_mod) + +q_mean_logs <- chydro %>% + mutate(q_log10=log10(q_mean), runofflog10 = log10(runoff_ratio)) + +q_mod_log <- lm(q_log10 ~ runofflog10 * p_mean, data = chydro) + +ggplot(chydro, aes(x = q_mean, y = runoff_ratio, color = p_mean)) + + geom_point() + + scale_x_log10() + + scale_y_log10() + + geom_smooth(method = 'lm') +``` + +```{r} + +#How Matt did it in class + +q_mean <- read_delim('data/hydro.txt', delim = ';') %>% + select(gauge_id, q_mean) %>% + inner_join(read_delim('data/climate.txt', delim = ';')) %>% + inner_join(read_delim('data/soil.txt', delim = ';')) + +#building linear model, predicting q_mean as a function of p_mean + +names(q_mean) + +##this is testing to see if low_prec_timing influences the q_mean and p_mean... +naive_mod <- lm(q_mean ~ p_mean * low_prec_timing, data = q_mean) + +summary(naive_mod) + +##the above model shows us a lot of intereactions that are NOT significant, so you would not want to use this + +##if you change the * to + -> you get a change in intercept but not a change in slope + +##intercept means the average q_mean without any precip, water_frac or aridity -> interpretation get's increasingly ghard when you add an interpretation variable (* in lm formula). + +##this complicated interaction is why we may want to switch to machine learning... + +#logged data +q_mean_logs <- q_mean %>% + mutate(p_log10=log10(p_mean), aridlog10 = log10(aridity), + q_mean10 = log10(q_mean)) + +naive_mod <- lm(q_mean10 ~ aridlog10 * p_log10, data = q_mean_logs) + +ggplot(q_mean, aes(x = p_mean, y = q_mean, color = aridity)) + + scale_x_log10() + + scale_y_log10() + + geom_smooth(method = 'lm') + +##this model is working better, but there is still some variation that we are not explaining... + +#to improve the model, check the indicators ands ee what relationship exists ``` **Answer:** -Slope Estimate: +- Slope Estimates: -- A one-unit increase in **`slope_fdc`** is associated with a decrease - of \~0.5330 units in **`q_mean`** + - Intercept: 2.873e-04 -- A one-unit increase in **`runoff_ratio`** is associated with an - increase of \~0.9667 units in **`q_mean`**. + - runoff_ratio: -1.530e-03 -- A one-unit increase in **`baseflow_index`** is associated with a - decrease of \~0.3779 units in **`q_mean`** + - p_mean: -9.969e-05 -r-squared = 0.8056 + - runoff_ratio:p_mean: 1.000e+00 -p-value = \< 2e-16 +- R-squared value: + + - Multiple R-squared: 1 + + - Adjusted R-squared: 1 + +- P-values: + + - Intercept: 0.4034 + + - runoff_ratio: 0.0358 + + - p_mean: 0.4274 + + - runoff_ratio:p_mean: \< 2.2e-16 + +- Interpretation: ## Build a CART model to predict flow. @@ -79,6 +151,50 @@ Read the logic intro above, and the code demo as well, to build a CART model version of your lm. Use the code to visualize your CART output. ```{r} +#install.packages('yardstick') +#install.packages('rpart.plot') +library(yardstick) +library(rpart.plot) + +#matt's example +q_4 <- q_mean_logs %>% + select(q_mean10, p_log10, aridlog10, sand_frac, clay_frac, soil_depth_pelletier) %>% + #to make it a classification tree - converting data from continuous to a class (binning it) -> gives us equal bins in each classification, which is necessary for machine learning + mutate(q_class = cut_number(q_mean10, n=4)) %>% + select(-q_mean10) + +train <- q_4 %>% + sample_frac(.7) + +test <- q_4 %>% + anti_join(train) + +cart_simple <- rpart(q_class ~ ., data = train, cp = 0.001, method = 'class') + +#visualize table/classes +table(q_4$q_class) + +#cart_simple <- rpart(q_class ~., data = q_4, cp = 0.01, method = 'anova') + +plot(cart_simple) +text(cart_simple, cex = 0.8, use.n = TRUE, xpd = TRUE) + +#how to understand model's performance - comparing predictions with actual data +q_4$pred <- predict(cart_simple, q_4, 'class') +cm <- conf_mat(q_4, q_class, pred) + +autoplot(cm, type = 'heatmap') + + scale_fill_gradient(low = 'grey', high = 'blue') + +accuracy(test_d, group, guess) + +test$pred <- predict(cart_simple, test, 'class') +cm <- conf_mat(q_4, q_class, pred) +``` + +```{r} +##need to bin my data using cart_numbers step into 4ish? ... change it to using the chydro dataset, and variables from only the climate, with q_mean from hydro! +#my cart model cart_mod <- rpart(q_mean ~ slope_fdc+runoff_ratio+baseflow_index, data=hydro, method = "anova") # visualize cross-validation results @@ -94,8 +210,7 @@ rsq.rpart(cart_mod) # visualize cross-validation results plot(cart_mod, uniform=TRUE, main="Regression Tree for Mean Flow ") -text(cart_mod, use.n=TRUE, all=TRUE, cex=.5) -##the titles for my text are not correct on this plot, bit sure why but I haven't been able to figure it out. +text(cart_mod, cex = 0.8, use.n = TRUE, xpd = TRUE) ``` ## Build a RandomForest From ee34eb1a77c2537361a52d6aca8d84b5264ee3c9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CErika?= Date: Tue, 30 Apr 2024 19:32:59 -0600 Subject: [PATCH 13/16] finished cart model --- 2_modelling_flow.Rmd | 56 ++++++++++++++++++++++++++++++-------------- 1 file changed, 38 insertions(+), 18 deletions(-) diff --git a/2_modelling_flow.Rmd b/2_modelling_flow.Rmd index d17192a..77a18f5 100644 --- a/2_modelling_flow.Rmd +++ b/2_modelling_flow.Rmd @@ -51,7 +51,8 @@ q_mod <- lm(q_mean ~ runoff_ratio * p_mean, data = chydro) summary(q_mod) q_mean_logs <- chydro %>% - mutate(q_log10=log10(q_mean), runofflog10 = log10(runoff_ratio)) + mutate(q_log10=log10(q_mean)) %>% + mutate(runofflog10 = log10(runoff_ratio)) q_mod_log <- lm(q_log10 ~ runofflog10 * p_mean, data = chydro) @@ -91,9 +92,9 @@ summary(naive_mod) #logged data q_mean_logs <- q_mean %>% mutate(p_log10=log10(p_mean), aridlog10 = log10(aridity), - q_mean10 = log10(q_mean)) + q_log10 = log10(q_mean)) -naive_mod <- lm(q_mean10 ~ aridlog10 * p_log10, data = q_mean_logs) +naive_mod <- lm(q_log10 ~ aridlog10 * p_log10, data = q_mean_logs) ggplot(q_mean, aes(x = p_mean, y = q_mean, color = aridity)) + scale_x_log10() + @@ -131,9 +132,9 @@ ggplot(q_mean, aes(x = p_mean, y = q_mean, color = aridity)) + - p_mean: 0.4274 - - runoff_ratio:p_mean: \< 2.2e-16 +- runoff_ratio:p_mean: \< 2.2e-16 -- Interpretation: +Interpretation: ## Build a CART model to predict flow. @@ -192,25 +193,44 @@ test$pred <- predict(cart_simple, test, 'class') cm <- conf_mat(q_4, q_class, pred) ``` +My Cart Model + ```{r} -##need to bin my data using cart_numbers step into 4ish? ... change it to using the chydro dataset, and variables from only the climate, with q_mean from hydro! -#my cart model -cart_mod <- rpart(q_mean ~ slope_fdc+runoff_ratio+baseflow_index, data=hydro, method = "anova") -# visualize cross-validation results -plotcp(cart_mod) +#create dataset +c_data <- q_mean_logs %>% + select(q_log10, p_mean, runofflog10, frac_snow, low_prec_freq, low_q_dur ) %>% + #to make it a classification tree - converting data from continuous to a class (binning it) -> gives us equal bins in each classification, which is necessary for machine learning + mutate(q_class = cut_number(q_log10, n=4)) %>% + select(-q_log10) + -#summarize data -summary(cart_mod) +train <- c_data %>% + sample_frac(.7) -# create additional plots -rsq.rpart(cart_mod) # visualize cross-validation results +test <- c_data %>% + anti_join(train) + +cart_simple_1 <- rpart(q_class ~ ., data = train, cp = 0.001, method = 'class') + +#visualize table/classes +table(c_data$q_class) + + +plot(cart_simple_1) +text(cart_simple_1, cex = 0.8, use.n = TRUE, xpd = TRUE) + +#how to understand model's performance - comparing predictions with actual data +c_data$pred <- predict(cart_simple_1, c_data, 'class') +cm_1 <- conf_mat(c_data, q_class, pred) + +autoplot(cm_1, type = 'heatmap') + + scale_fill_gradient(low = 'grey', high = 'blue') -# plot tree -plot(cart_mod, uniform=TRUE, - main="Regression Tree for Mean Flow ") +accuracy(test, group, guess) -text(cart_mod, cex = 0.8, use.n = TRUE, xpd = TRUE) +test$pred <- predict(cart_simple_1, test, 'class') +cm_1 <- conf_mat(c_data, q_class, pred) ``` ## Build a RandomForest From fbd0930f32ae10b563fcbc561bc5e2a0df13670b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CErika?= Date: Wed, 1 May 2024 12:13:03 -0600 Subject: [PATCH 14/16] finished assignment 2 --- 2_modelling_flow.Rmd | 203 ++++++++++++++++++++++++++++++++++++------- 1 file changed, 174 insertions(+), 29 deletions(-) diff --git a/2_modelling_flow.Rmd b/2_modelling_flow.Rmd index 77a18f5..940b8c2 100644 --- a/2_modelling_flow.Rmd +++ b/2_modelling_flow.Rmd @@ -45,22 +45,32 @@ chydro <- inner_join(hydro,climate) #plot simple relationship #create parsimonious model -q_mod <- lm(q_mean ~ runoff_ratio * p_mean, data = chydro) +r_mod <- lm(runoff_ratio ~ frac_snow * p_mean, data = chydro) #Get the summary of the model -summary(q_mod) +summary(r_mod) -q_mean_logs <- chydro %>% - mutate(q_log10=log10(q_mean)) %>% - mutate(runofflog10 = log10(runoff_ratio)) +ggplot(chydro, aes(x = runoff_ratio, y = frac_snow, color = p_mean)) + + geom_point() + + scale_x_log10() + + scale_y_log10() + + geom_smooth(method = 'lm') + +#logged data +r_logs <- chydro %>% + mutate(runoff_log=log10(runoff_ratio), p_log = log10(p_mean)) %>% + na.omit() -q_mod_log <- lm(q_log10 ~ runofflog10 * p_mean, data = chydro) -ggplot(chydro, aes(x = q_mean, y = runoff_ratio, color = p_mean)) + +r_log_mod <- lm(runoff_log ~ frac_snow * p_log, data = r_logs) + +ggplot(r_logs, aes(x = runoff_log, y = frac_snow, color = p_mean)) + geom_point() + scale_x_log10() + scale_y_log10() + geom_smooth(method = 'lm') + +summary(r_log_mod) ``` ```{r} @@ -110,31 +120,45 @@ ggplot(q_mean, aes(x = p_mean, y = q_mean, color = aridity)) + - Slope Estimates: - - Intercept: 2.873e-04 + - Intercept: -1.42623 - - runoff_ratio: -1.530e-03 + - frac_snow: 1.19944 - - p_mean: -9.969e-05 + - p_log: 1.64662 - - runoff_ratio:p_mean: 1.000e+00 + - frac_snow:p_log: -1.09107 -- R-squared value: +- R-squared Value: - - Multiple R-squared: 1 + - Multiple R-squared: 0.6716 - - Adjusted R-squared: 1 + - Adjusted R-squared: 0.6701 - P-values: - - Intercept: 0.4034 + - Intercept: \< 2e-16 - - runoff_ratio: 0.0358 + - frac_snow: \< 2e-16 - - p_mean: 0.4274 + - p_log: \< 2e-16 -- runoff_ratio:p_mean: \< 2.2e-16 + - frac_snow:p_log: 4.8e-06 -Interpretation: +Interpretation of results: + +These results indicate a significant linear relationship between the +predictors ( frac_snow and log_precip_mean) and the runoff ratio as a +response variable. When frac_snow and p_log are both zero, runoff_ratio +is estimated to be approximately -1.43. + +For every one unit increase of frac_snow, runoff_ratio increases by \~ +1.20 units. For every one unit increase of p_log, runoff ratio increases +by \~ 1.65 units. As p_log increases, the impact of frac_snow on runoff +ratio decreases by -1.09 units. + +The r-squared value indicates that 67.16% of the variance of the +response variable (runoff ratio) is explained by the frac_snow and p_log +values. ## Build a CART model to predict flow. @@ -158,11 +182,15 @@ library(yardstick) library(rpart.plot) #matt's example + +set.seed(556749) + q_4 <- q_mean_logs %>% - select(q_mean10, p_log10, aridlog10, sand_frac, clay_frac, soil_depth_pelletier) %>% + select(q_log10, p_log10, aridlog10, sand_frac, clay_frac, soil_depth_pelletier) %>% #to make it a classification tree - converting data from continuous to a class (binning it) -> gives us equal bins in each classification, which is necessary for machine learning - mutate(q_class = cut_number(q_mean10, n=4)) %>% - select(-q_mean10) + mutate(q_class = cut_number(q_log10, n=4)) %>% + select(-q_log10) %>% + na.omit() train <- q_4 %>% sample_frac(.7) @@ -198,11 +226,14 @@ My Cart Model ```{r} #create dataset -c_data <- q_mean_logs %>% - select(q_log10, p_mean, runofflog10, frac_snow, low_prec_freq, low_q_dur ) %>% + +set.seed(564739) + +c_data <- r_logs %>% + select(runoff_ratio, p_log, frac_snow, aridity, low_prec_freq, low_q_dur) %>% #to make it a classification tree - converting data from continuous to a class (binning it) -> gives us equal bins in each classification, which is necessary for machine learning - mutate(q_class = cut_number(q_log10, n=4)) %>% - select(-q_log10) + mutate(r_class = cut_number(runoff_ratio, n=4)) %>% + select(-runoff_ratio) train <- c_data %>% @@ -211,10 +242,10 @@ train <- c_data %>% test <- c_data %>% anti_join(train) -cart_simple_1 <- rpart(q_class ~ ., data = train, cp = 0.001, method = 'class') +cart_simple_1 <- rpart(r_class ~ ., data = train, cp = 0.001, method = 'class') #visualize table/classes -table(c_data$q_class) +table(c_data$r_class) plot(cart_simple_1) @@ -222,7 +253,7 @@ text(cart_simple_1, cex = 0.8, use.n = TRUE, xpd = TRUE) #how to understand model's performance - comparing predictions with actual data c_data$pred <- predict(cart_simple_1, c_data, 'class') -cm_1 <- conf_mat(c_data, q_class, pred) +cm_1 <- conf_mat(c_data, r_class, pred) autoplot(cm_1, type = 'heatmap') + scale_fill_gradient(low = 'grey', high = 'blue') @@ -245,5 +276,119 @@ sure we split our data at least into train and test datasets and ideally into train-test-val. ```{r} +library(randomForest) + +## matt's example +rf_class <- randomForest(q_class ~ ., data = train, + maxnodes = 9, + nPerm = 2, + mtry = 5, + #add importance level to model... + importance = T) + +(rf_class) + +test$rf_pred <- predict(rf_class, test) + +cm_rf <- conf_mat(test, q_class, rf_pred) +autoplot(cm_rf, type = "heatmap") + + scale_fill_gradient(low = "#D6EAF8", high = "#2E86C1") + +#how to see the accuracy -> higher estimate number (closer to 1), the better the predictive model +accuracy(test, q_class, rf_pred) +``` + +ways to improve model - add more variables (#1 way), add variable +importance to randomForest() function (... importance = T), can remove +variables that don't do anything (are not significant...) -\> do lots of +these iterations to see how the RandomForest model runs best! + +a critical thing to think about -\> randdomForest is RANDOM sampling... +so it is not reproducable. So, you need to do a set.seed(), so every +time it samples a fraction, its the same fraction... the number in +set.seed() needs to be any random number + +can also add maxnodes = 9 into random forest model, nPerm (number of +permutations, i.e. how many times the model runs), and mtry = squareroot +of number of columns (do the square root of the number of columns, or +make it bigger than that number to improve it) + +```{r} +##continuous predictor example + +my_rf <- q_mean_logs %>% + select(q_log10, p_mean, frac_snow, soil_porosity, soil_conductivity, clay_frac, max_water_content) %>% + mutate(q_class = cut_number(q_log10, n=4)) %>% + select(-q_log10) %>% + na.omit() + +train_cont <- q_4 %>% + sample_frac(.7) + +test_cont <- q_4 %>% + anti_join(train) + +rf_numer <- randomForest(q_class ~ ., data = train, + maxnodes = 9, + nPerm = 2, + mtry = 5, + #add importance level to model... + importance = T) + +(rf_class) + +test_cont$rf_pred <- predict(rf_numer, test_cont) +train_cont$rf_pred <- pred(rf_numer, train_cont) + +cm_rf <- conf_mat(test, q_class, rf_pred) + +ggplot(test_cont, aes(x = q_log10, y = rd_pred)) + + geom_point() + + geom_abline(slope = 1) + +##want to minimize the distance between the test and train sets... +cor(test_cont$rf_pred, test_cont$q_mean10)^2 +cor(train_cont$rf_pred, test_cont$q_mean10)^2 +##if train set results are close to 100%, you're over training... +``` + +```{r} +#my randomForest model +library(randomForest) + +my_rf <- r_logs%>% + select(runoff_ratio, frac_snow, p_log, aridity, low_prec_dur, low_prec_freq) %>% + #to make it a classification tree - converting data from continuous to a class (binning it) -> gives us equal bins in each classification, which is necessary for machine learning + mutate(r_class = cut_number(runoff_ratio, n=4)) %>% + select(-runoff_ratio) %>% + na.omit() + +train_cont <- my_rf %>% + sample_frac(.7) + +test_cont <- my_rf %>% + anti_join(train) + +rf_numer_2 <- randomForest(r_class ~ ., data = train, + maxnodes = 9, + nPerm = 2, + mtry = 5, + #add importance level to model... + importance = T) + +(rf_numer_2) + +test_cont$rf_pred <- predict(rf_numer_2, test_cont) +train_cont$rf_pred <- predict(rf_numer_2, train_cont) + +cm_rf <- conf_mat(test, r_class, rf_pred) + +ggplot(test_cont, aes(x = runoff_ratio, y = rd_pred)) + + geom_point() + + geom_abline(slope = 1) +##want to minimize the distance between the test and train sets... +cor(test_cont$rf_pred, test_cont$runoff_ratio)^2 +cor(train_cont$rf_pred, test_cont$runoff_ratio)^2 +##if train set results are close to 100%, you're over training... ``` From 33e709f2a11b1a3fa14e4beb3057ec724ebb16da Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CErika?= Date: Wed, 1 May 2024 12:23:15 -0600 Subject: [PATCH 15/16] removing random push/pull code --- 2_modelling_flow.Rmd | 124 ------------------------------------------- 1 file changed, 124 deletions(-) diff --git a/2_modelling_flow.Rmd b/2_modelling_flow.Rmd index fd7fc7b..940b8c2 100644 --- a/2_modelling_flow.Rmd +++ b/2_modelling_flow.Rmd @@ -12,14 +12,8 @@ editor_options: library(tidyverse) library(rpart) -<<<<<<< HEAD library(ggplot2) -======= -library(rpart.plot) -library(yardstick) -library(randomForest) ->>>>>>> ff2e88f22c160480a50644e7b06f0843757377d5 knitr::opts_chunk$set(echo = TRUE) ``` @@ -81,7 +75,6 @@ summary(r_log_mod) ```{r} -<<<<<<< HEAD #How Matt did it in class q_mean <- read_delim('data/hydro.txt', delim = ';') %>% @@ -121,24 +114,6 @@ ggplot(q_mean, aes(x = p_mean, y = q_mean, color = aridity)) + ##this model is working better, but there is still some variation that we are not explaining... #to improve the model, check the indicators ands ee what relationship exists -======= -q_mean <- read_delim('data/hydro.txt', delim = ';') %>% - select(gauge_id, q_mean) %>% - inner_join(read_delim('data/climate.txt', delim = ';')) %>% - inner_join(read_delim('data/soil.txt',delim = ';')) - - -q_mean_logs <- q_mean %>% - mutate(p_log10 = log10(p_mean), - aridlog10 = log10(aridity), - q_mean10 = log10(q_mean)) - -naive_mod <- lm(q_mean10 ~ aridlog10 * p_log10, data = q_mean_logs) - - - - ->>>>>>> ff2e88f22c160480a50644e7b06f0843757377d5 ``` **Answer:** @@ -249,39 +224,8 @@ cm <- conf_mat(q_4, q_class, pred) My Cart Model ```{r} -set.seed(22157) -q_4 <- q_mean_logs %>% - select(q_mean10, p_log10, aridlog10, soil_depth_pelletier, max_water_content, organic_frac, frac_snow,pet_mean) %>% - mutate(q_class = cut_number(q_mean10,n = 4)) %>% - select(-q_mean10) %>% - na.omit() -<<<<<<< HEAD #create dataset -======= -train <- q_4 %>% - sample_frac(.7) - -test <- q_4 %>% - anti_join(train) - -cart_simple <- rpart(q_class ~., data = train, cp = 0.01, - method = 'class') - - -plot(cart_simple) -text(cart_simple, cex = 0.8, use.n = TRUE, xpd = TRUE) - - -test$pred <- predict(cart_simple, test, 'class') -cm <- conf_mat(test, q_class,pred) - -autoplot(cm, type = "heatmap") + - scale_fill_gradient(low="#D6EAF8",high = "#2E86C1") - -accuracy(test,q_class,pred) -``` ->>>>>>> ff2e88f22c160480a50644e7b06f0843757377d5 set.seed(564739) @@ -322,7 +266,6 @@ cm_1 <- conf_mat(c_data, q_class, pred) ## Build a RandomForest -<<<<<<< HEAD CARTs are a single tree, what if we had thousands? Would we get better performance (yes!) @@ -331,22 +274,10 @@ read this part of the site and use the code demo to build your own RandomForest. Remember, for a RandomForest type model we want to make sure we split our data at least into train and test datasets and ideally into train-test-val. -======= -CARTs are a single tree, what if we had thousands? Would we get better performance (yes!) - -The same CART logic site above introduces random forests as well. Please -read this part of the site and use the code demo to build your own RandomForest. -Remember, for a RandomForest type model we want to make sure we split our data -at least into train and test datasets and ideally into train-test-val. - - -### Classification Forest ->>>>>>> ff2e88f22c160480a50644e7b06f0843757377d5 ```{r} library(randomForest) -<<<<<<< HEAD ## matt's example rf_class <- randomForest(q_class ~ ., data = train, maxnodes = 9, @@ -366,61 +297,6 @@ autoplot(cm_rf, type = "heatmap") + #how to see the accuracy -> higher estimate number (closer to 1), the better the predictive model accuracy(test, q_class, rf_pred) -======= -summary(train) -rf_class <- randomForest(q_class ~ ., data = train, - maxnodes = 9, - nPerm = 2, - mtry = 5, - importance = T) - -test$rf_pred <- predict(rf_class, test) - -cm_rf <- conf_mat(test, q_class,rf_pred) - -autoplot(cm_rf, type = "heatmap") + - scale_fill_gradient(low="#D6EAF8",high = "#2E86C1") - -accuracy(test,q_class,rf_pred) - -``` - -### Continuous pred - -```{r} -q_rf <- q_mean_logs %>% - select(q_mean10, p_log10, aridlog10, soil_depth_pelletier, frac_snow,pet_mean) %>% - na.omit() - -train_cont <- q_rf %>% - sample_frac(.7) - -test_cont <- q_rf %>% - anti_join(train) - - -rf_numer <- randomForest(q_mean10 ~ ., data = train_cont, - maxnodes = 50, - nPerm = 5, - mtry = 8, - importance = T) - -rf_numer$importance - -test_cont$rf_pred <- predict(rf_numer, test_cont) -train_cont$rf_pred <- predict(rf_numer, train_cont) - - -ggplot(test_cont, aes(x = q_mean10, - y = rf_pred)) + - geom_point() + - geom_abline(slope = 1) - - -cor(test_cont$rf_pred,test_cont$q_mean10)^2 -cor(train_cont$rf_pred,train_cont$q_mean10)^2 - ->>>>>>> ff2e88f22c160480a50644e7b06f0843757377d5 ``` ways to improve model - add more variables (#1 way), add variable From fb1040b20595f61472fb7df72a725c064a3ff247 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CErika?= Date: Wed, 1 May 2024 12:25:16 -0600 Subject: [PATCH 16/16] finished assingment 2 --- 2_modelling_flow.Rmd | 1 - 1 file changed, 1 deletion(-) diff --git a/2_modelling_flow.Rmd b/2_modelling_flow.Rmd index 940b8c2..7f874a6 100644 --- a/2_modelling_flow.Rmd +++ b/2_modelling_flow.Rmd @@ -355,7 +355,6 @@ cor(train_cont$rf_pred, test_cont$q_mean10)^2 ```{r} #my randomForest model -library(randomForest) my_rf <- r_logs%>% select(runoff_ratio, frac_snow, p_log, aridity, low_prec_dur, low_prec_freq) %>%
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