Update to version 0.2.0

DESCRIPTION

@@ -1,21 +1,21 @@
 Package: lddmm
 Type: Package
 Title: Longitudinal Drift-Diffusion Mixed Models (LDDMM)
-Version: 0.1.0.9000
-Date: 2021-11-02
+Version: 0.2.0
+Date: 2023-01-27
 Authors@R: c(person("Giorgio", "Paulon", email = "giorgio.paulon@utexas.edu",
                     role = c("aut", "cre")), 
              person("Abhra", "Sarkar", email = "abhra.sarkar@utexas.edu",
                     role = c("aut", "ctb")))
-Description: Implementation of the drift-diffusion mixed model for category learning as described in Paulon et al. (2021) <https://doi.org/10.1080/01621459.2020.1801448>.
+Description: Implementation of the drift-diffusion mixed model for category learning as described in Paulon et al. (2021) <doi:10.1080/01621459.2020.1801448>.
 Depends: R (>= 3.5.0)
 Language: en-US
 License: MIT + file LICENSE
 Encoding: UTF-8
 Imports: Rcpp (>= 1.0.6), RcppProgress, rgen, gtools, LaplacesDemon, dplyr, plyr, tidyr, ggplot2, latex2exp, reshape2, RColorBrewer
 LazyData: true
 LinkingTo: Rcpp, RcppArmadillo, RcppProgress, rgen
-RoxygenNote: 7.1.2
+RoxygenNote: 7.2.3
 Roxygen: list(markdown = TRUE)
 Suggests: 
     rmarkdown,

LICENSE

@@ -1,2 +1,2 @@
-YEAR: 2021
+YEAR: 2022
 COPYRIGHT HOLDER: Giorgio Paulon

LICENSE.md

@@ -1,6 +1,6 @@
 # MIT License
 
-Copyright (c) 2021 Giorgio Paulon
+Copyright (c) 2022 Giorgio Paulon
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

NAMESPACE

@@ -1,5 +1,7 @@
-useDynLib(lddmm, .registration=TRUE)
-importFrom(Rcpp, evalCpp)
+# Generated by roxygen2: do not edit by hand
+
+importFrom(Rcpp,sourceCpp)
+useDynLib(lddmm, .registration = TRUE)
 importFrom("stats", "dgamma", "rnorm", "runif", "sd", "setNames", 
            "quantile")
 importFrom("utils", "data", "setTxtProgressBar", "txtProgressBar")

News.md

@@ -4,13 +4,16 @@
 
 ## Major changes
 
+* Now the boundary parameter in the model can either be unrestricted (`boundaries = "flexible"`), constant over time (`boundaries = "constant"`) or fixed at the same value across predictors (`boundaries = "fixed"`).
+
 ## Minor changes
 
 * Added the DOI for the article reference
+* Clarified that response times should be the log transformed of time (in milliseconds) for numerical stability. 
 
 ## Bug fixes
 
 
 # LDDMM 0.1.0
 
-* First version
+* First version

R/fcts.R

@@ -5,7 +5,7 @@ require(mvtnorm)
 require(gtools)
 require(ggplot2)
 require(latex2exp)
-library(RColorBrewer)
+require(RColorBrewer)
 require(LaplacesDemon)
 require(plyr)
 
@@ -21,7 +21,7 @@ require(plyr)
 #' * block: vector of size n containing the training blocks (longitudinal units)
 #' * s: vector of size n containing the stimuli
 #' * d: vector of size n containing the decisions
-#' * r_time: vector of size n containing the response times
+#' * r_time: vector of size n containing the log transformed of the response times (in milliseconds)
 #' * cens: vector of size n containing the censoring indicators (1 censored, 0 non censored)
 #' @return Individual and population level raw accuracies
 plot_accuracy <- function(data){
@@ -69,7 +69,7 @@ plot_accuracy <- function(data){
 #' * block: vector of size n containing the training blocks (longitudinal units)
 #' * s: vector of size n containing the stimuli
 #' * d: vector of size n containing the decisions
-#' * r_time: vector of size n containing the response times
+#' * r_time: vector of size n containing the log transformed of the response times (in milliseconds)
 #' * cens: vector of size n containing the censoring indicators (1 censored, 0 non censored)
 #' @return Population level raw response times
 plot_RT <- function(data){
@@ -294,11 +294,13 @@ sample_smooth_var <- function(sigma2_ua_old, sigma2_us_old,
 #' * block: vector of size n containing the training blocks (longitudinal units)
 #' * s: vector of size n containing the stimuli
 #' * d: vector of size n containing the decisions
-#' * r_time: vector of size n containing the response times
+#' * r_time: vector of size n containing the log transformed of the response times (in milliseconds)
 #' * cens: vector of size n containing the censoring indicators (1 censored, 0 non censored)
 #' @param hypers hyperparameters of the MCMC: list containing "s_sigma_mu" and "s_sigma_b", 
 #'               which are the smoothness parameters for drifts and boundaries, respectively)
-#' @param fix_boundary whether to fix the boundary parameters to a single scalar or not
+#' @param boundaries whether to fit the unrestricted model (flexible), assume constant 
+#'                   boundaries over time (constant) or fix the boundaries to the same level 
+#'                   across predictors (fixed)
 #' @param Niter total number of iterations
 #' @param burnin burnin of the chain
 #' @param thin thinning factor
@@ -319,7 +321,7 @@ sample_smooth_var <- function(sigma2_ua_old, sigma2_us_old,
 #' * pred_time: predicted population-level response times
 #' * pred_ans_ind: predicted individual-level categories
 #' * pred_time_ind: predicted individual-level response times
-LDDMM <- function(data, hypers, fix_boundary = FALSE, 
+LDDMM <- function(data, hypers, boundaries = 'flexible', 
                   Niter = 5000, burnin = 2000, thin = 5){
 
   # Check for data issues
@@ -337,12 +339,18 @@ LDDMM <- function(data, hypers, fix_boundary = FALSE,
   }
 
   # Call one of the main functions
-  if (!fix_boundary){
+  if (boundaries == 'flexible'){
     fit <- LDDMM_full(data, hypers, Niter, burnin, thin)
   }
-  else {
+  else if (boundaries == 'constant') {
+    fit <- LDDMM_fix_bound(data, hypers, Niter, burnin, thin)
+  }
+  else if (boundaries == 'fixed') {
     fit <- LDDMM_fix_all_bound(data, hypers, Niter, burnin, thin)
   }
+  else {
+    stop("The argument boundaries can be only one of the following: flexible, constant, or fixed")
+  }
 
   return (fit)
 }
@@ -1550,29 +1558,7 @@ LDDMM_fix_bound <- function(data, hypers, Niter = 5000, burnin = 2000, thin = 5)
       }
     }
 
-    # 2(a) Update the single scalar boundary parameter
-    # b_prop <- rnorm(1, b_old, sd_MH_beta_b)
-    # 
-    # # Modify the proposed values in the corresponding positions
-    # B_beta_b_prop_dat <- matrix(b_prop, n, d_j[1])
-    # 
-    # # This is the proposed value for b
-    # b_prop_dat <- exp(B_beta_b_prop_dat)
-    # 
-    # logpost_prop <- log_likelihood(tau, mu_dat, 
-    #                                b_prop_dat, delta_dat, 
-    #                                cens, D, TRUE)
-    # logpost_old <- log_likelihood(tau, mu_dat, 
-    #                               b_dat, delta_dat, 
-    #                               cens, D, TRUE)
-    # 
-    # alpha_acc <- min(0, logpost_prop - logpost_old)
-    # l_u <- log(runif(1))
-    # 
-    # if (l_u < alpha_acc){
-    #   b_old <- b_prop
-    #   b_dat <- b_prop_dat
-    # }
+    # 2(a) Update the constant boundary parameters
     for (d1 in 1:d_j[1]){
       idx_i <- which( (D[,1] == d1) )
       tau_temp <- tau[idx_i]
@@ -2700,7 +2686,7 @@ LDDMM_fix_all_bound <- function(data, hypers, Niter = 5000, burnin = 2000, thin
 #' * block: vector of size n containing the training blocks (longitudinal units)
 #' * s: vector of size n containing the stimuli
 #' * d: vector of size n containing the decisions
-#' * r_time: vector of size n containing the response times
+#' * r_time: vector of size n containing the log transformed of the response times (in milliseconds)
 #' * cens: vector of size n containing the censoring indicators (1 censored, 0 non censored)
 #' @param fit fit from the lddmm function
 #' @param par parameter to output ('drift', or 'boundary')
@@ -2744,7 +2730,7 @@ extract_post_mean = function(data, fit, par = c('drift', 'boundary')){
 #' * block: vector of size n containing the training blocks (longitudinal units)
 #' * s: vector of size n containing the stimuli
 #' * d: vector of size n containing the decisions
-#' * r_time: vector of size n containing the response times
+#' * r_time: vector of size n containing the log transformed of the response times (in milliseconds)
 #' * cens: vector of size n containing the censoring indicators (1 censored, 0 non censored)
 #' @param fit fit from the lddmm function
 #' @param par parameter to output ('drift', or 'boundary')
@@ -2790,7 +2776,7 @@ extract_post_draws = function(data, fit, par = c('drift', 'boundary')){
 #' * block: vector of size n containing the training blocks (longitudinal units)
 #' * s: vector of size n containing the stimuli
 #' * d: vector of size n containing the decisions
-#' * r_time: vector of size n containing the response times
+#' * r_time: vector of size n containing the log transformed of the response times (in milliseconds)
 #' * cens: vector of size n containing the censoring indicators (1 censored, 0 non censored)
 #' @param fit fit from the lddmm function
 #' @param par parameter to output ('drift', or 'boundary')

README.Rmd

@@ -15,7 +15,7 @@ knitr::opts_chunk$set(
 
 # LDDMM
 
-An R package for Longitudinal Drift-Diffusion Mixed Models (LDDMM), v0.1.
+An R package for Longitudinal Drift-Diffusion Mixed Models (LDDMM), v0.2.0.
 
 **Authors**: [Giorgio Paulon](https://giorgiopaulon.github.io/), [Abhra Sarkar](https://abhrastat.github.io/)
 
@@ -47,6 +47,7 @@ If you are using a Windows machine, you might have to also install and configure
 ### Usage
 
 The following is a minimal example of a simple model fit. 
+For numerical stability, the response times should be provided as the log transform of time (in milliseconds) so that, for most applications, typical values lie in $[0, 10]$. 
 
 ```{r, eval = TRUE, results = 'hide', fig.show = 'hide', warning = FALSE, message = FALSE}
 # Load libraries
@@ -81,7 +82,6 @@ samp_size <- (Niter - burnin) / thin
 set.seed(123)
 fit <- LDDMM(data = data, 
              hypers = hypers, 
-             fix_boundary = FALSE, 
              Niter = Niter, 
              burnin = burnin, 
              thin = thin)
@@ -91,8 +91,10 @@ plot_post_pars(data, fit, par = 'drift')
 plot_post_pars(data, fit, par = 'boundary')
 ```
 
+
 To extract relevant posterior draws or posterior summaries instead of simply plotting them, one can use the functions `extract_post_mean` or `extract_post_draws`. 
-An auxiliary function that fixes the boundary parameters can be called with the option `fix_boundary = TRUE`.
+Auxiliary functions that assume constant boundary parameters over time or fix the boundaries to the same level across predictors can be called with the options `boundaries = "constant"` and `boundaries = "fixed"`, respectively.
+
 
 ### Questions or bugs
 

README.md

@@ -2,7 +2,7 @@
 # LDDMM
 
 An R package for Longitudinal Drift-Diffusion Mixed Models (LDDMM),
-v0.1.
+v0.2.0.
 
 **Authors**: [Giorgio Paulon](https://giorgiopaulon.github.io/), [Abhra
 Sarkar](https://abhrastat.github.io/)
@@ -58,6 +58,7 @@ configure `Rtools` using the following
 ### Usage
 
 The following is a minimal example of a simple model fit.
+For numerical stability, the response times should be provided as the log transform of time (in milliseconds) so that, for most applications, typical values lie in $[0, 10]$. 
 
 ``` r
 # Load libraries
@@ -92,7 +93,6 @@ samp_size <- (Niter - burnin) / thin
 set.seed(123)
 fit <- LDDMM(data = data, 
              hypers = hypers, 
-             fix_boundary = FALSE, 
              Niter = Niter, 
              burnin = burnin, 
              thin = thin)
@@ -104,8 +104,12 @@ plot_post_pars(data, fit, par = 'boundary')
 
 To extract relevant posterior draws or posterior summaries instead of
 simply plotting them, one can use the functions `extract_post_mean` or
-`extract_post_draws`. An auxiliary function that fixes the boundary
-parameters can be called with the option `fix_boundary = TRUE`.
+`extract_post_draws`. Auxiliary functions that assume constant boundary 
+parameters over time or fix the boundaries to the same level across predictors 
+can be called with the options `boundaries = "constant"` and 
+`boundaries = "fixed"`, respectively.
+
+
 
 ### Questions or bugs
 

cran-comments.md

@@ -1,10 +1,14 @@
-
 ## R CMD check results
 
-── R CMD check results ────────────────────────────────────────────────────────────────────────────────────────────── lddmm 0.1.0 ────
-Duration: 1m 7.1s
+── R CMD check results ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────── lddmm 0.2.0.9000 ────
+Duration: 1m 15.7s
+
+❯ checking dependencies in R code ... NOTE
+  Namespaces in Imports field not imported from:
+    ‘RColorBrewer’ ‘RcppProgress’ ‘rgen’
+    All declared Imports should be used.
 
-0 errors ✓ | 0 warnings ✓ | 0 notes ✓
+0 errors ✔ | 0 warnings ✔ | 1 note ✖
 
 R CMD check succeeded
 

vignettes/minimal_example.Rmd

@@ -52,7 +52,6 @@ samp_size <- (Niter - burnin) / thin
 set.seed(123)
 fit <- LDDMM(data = data, 
              hypers = hypers, 
-             fix_boundary = FALSE, 
              Niter = Niter, 
              burnin = burnin, 
              thin = thin)
@@ -63,4 +62,4 @@ plot_post_pars(data, fit, par = 'boundary')
 ```
 
 To extract relevant posterior draws or posterior summaries instead of simply plotting them, one can use the functions `extract_post_mean` or `extract_post_draws`. 
-An auxiliary function that fixes the boundary parameters can be called with the option `fix_boundary = TRUE`.
+Auxiliary functions that assume constant boundary parameters over time or fix the boundaries to the same level across predictors can be called with the options `boundaries = "constant"` and `boundaries = "fixed"`, respectively.