update modelcomp

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: GDINA
 Type: Package
 Title: The Generalized DINA Model Framework
-Version: 2.9.4
-Date: 2022-11-04
+Version: 2.9.5
+Date: 2023-11-09
 Authors@R: c(person(given = "Wenchao",family = "Ma", role = c("aut", "cre", "cph"),email = "[email protected]"),person(given = "Jimmy", family = "de la Torre", role = c("aut","cph")), person(given="Miguel",family = "Sorrel",role = "ctb"), person(given="Zhehan",family = "Jiang",role = "ctb"))
 Description: A set of psychometric tools for cognitive diagnosis modeling based on the generalized deterministic inputs, noisy and gate (G-DINA) model by de la Torre (2011) <DOI:10.1007/s11336-011-9207-7> and its extensions, including the sequential G-DINA model by Ma and de la Torre (2016) <DOI:10.1111/bmsp.12070> for polytomous responses, and the polytomous G-DINA model by Chen and de la Torre <DOI:10.1177/0146621613479818> for polytomous attributes. Joint attribute distribution can be independent, saturated, higher-order, loglinear smoothed or structured. Q-matrix validation, item and model fit statistics, model comparison at test and item level and differential item functioning can also be conducted. A graphical user interface is also provided. For tutorials, please check Ma and de la Torre (2020) <DOI:10.18637/jss.v093.i14>, Ma and de la Torre (2019) <DOI:10.1111/emip.12262>, Ma (2019) <DOI:10.1007/978-3-030-05584-4_29> and de la Torre and Akbay (2019). 
 License: GPL-3
@@ -14,7 +14,7 @@ Suggests: CDM, cdmTools, Matrix, testthat, poLCA, stringr, knitr, rmarkdown
 LinkingTo: Rcpp, RcppArmadillo
 URL: https://github.com/Wenchao-Ma/GDINA, https://wenchao-ma.github.io/GDINA/
 BugReports: https://github.com/Wenchao-Ma/GDINA/issues
-RoxygenNote: 7.2.1
+RoxygenNote: 7.2.3
 Encoding: UTF-8
 NeedsCompilation: yes
 Author: Wenchao Ma [aut, cre, cph], Jimmy de la Torre [aut, cph], Miguel Sorrel [ctb], Zhehan Jiang [ctb]

R/modelcomp.R

@@ -14,17 +14,16 @@
 #' if at least one reduced model was retained, two diferent rules can be implemented for selecting
 #' the best model specified in argument \code{decision.args}:
 #'
-#' (1) when \code{rule="simpler"}, which is the default,
+#' (1) when \code{rule="simpler"},
 #'
 #'  If (a) the DINA or DINO model
 #'  was one of the retained models, then the DINA or DINO model with the larger p
 #'  value was selected as the best model; but if (b) both DINA and DINO were rejected, the reduced
 #'  model with the largest p value was selected as the best model for this item. Note that
 #'  when the p-values of several reduced models were greater than 0.05, the DINA and DINO models were
-#'  preferred over the A-CDM, LLM, and R-RUM because of their simplicity. This procedure is originally
-#'  proposed by Ma, Iaconangelo, and de la Torre (2016).
+#'  preferred over the A-CDM, LLM, and R-RUM because of their simplicity.
 #'
-#'  (2) When \code{rule="largestp"}:
+#'  (2) When \code{rule="largestp"} (default),
 #'
 #'  The reduced model with the largest p-values is selected as the most appropriate model.
 #'
@@ -138,7 +137,7 @@
 #' @export
 modelcomp <- function(GDINA.obj=NULL,method = "Wald",items = "all", p.adjust.methods = "holm",
                       models=c("DINA","DINO","ACDM","LLM","RRUM"),
-                      decision.args = list(rule = "simpler", alpha.level = 0.05, adjusted = FALSE), DS = FALSE,
+                      decision.args = list(rule = "largestp", alpha.level = 0.05, adjusted = FALSE), DS = FALSE,
                       Wald.args = list(SE.type = 2,varcov = NULL),
                       LR.args = list(LR.approx = FALSE),
                       LM.args = list(reducedMDINA = NULL, reducedMDINO = NULL, reducedMACDM  = NULL, SE.type = 2)){

inst/shiny/ui.R

@@ -323,5 +323,6 @@ shinydashboard::tabItem(tabName = "plot",
 shinydashboard::dashboardPage(
   shinydashboard::dashboardHeader(title = "GDINA GUI"),
   sidebar,
-  body
+  body,
+  skin = "red"
 )