Merge pull request #33 from youngahn/develop

Merge (v0.5.1)

Author: youngahn

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: hBayesDM
 Title: Hierarchical Bayesian Modeling of Decision-Making Tasks
-Version: 0.5.0
-Date: 2017-12-25
+Version: 0.5.1
+Date: 2018-03-25
 Authors@R: c(
 	person("Woo-Young", "Ahn", email = "wooyoung.ahn@gmail.com", role = c("aut", "cre")),
 	person("Nate", "Haines", email = "haines.175@osu.edu", role = c("aut")),

NAMESPACE

@@ -33,12 +33,17 @@ export(prl_ewa)
 export(prl_fictitious)
 export(prl_fictitious_multipleB)
 export(prl_fictitious_rp)
+export(prl_fictitious_rp_woa)
+export(prl_fictitious_woa)
 export(prl_rp)
 export(prl_rp_multipleB)
 export(ra_noLA)
 export(ra_noRA)
 export(ra_prospect)
 export(rhat)
+export(ts_par4)
+export(ts_par6)
+export(ts_par7)
 export(ug_bayes)
 export(ug_delta)
 import(Rcpp)

R/hBayesDM.R

@@ -28,11 +28,13 @@
 #'                                Value-Plus_Perseverance --- \link{igt_vpp}}
 #'  \item{\strong{Peer influence task}}{OCU model --- \link{peer_ocu}}
 #'  \item{\strong{Probabilistic Reversal Learning}}{Fictitious Update --- \link{prl_fictitious} \cr
+#'                                                  Fictitious Update w/o alpha (indecision point) --- \link{prl_fictitious_woa} \cr
 #'                                                  Fictitious Update and multiple blocks per subject --- \link{prl_fictitious_multipleB} \cr
 #'                                                  Experience-Weighted Attraction --- \link{prl_ewa} \cr
 #'                                                  Reward-Punishment --- \link{prl_rp} \cr
 #'                                                  Reward-Punishment and multiple blocks per subject --- \link{prl_rp_multipleB} \cr
-#'                                                  Fictitious Update with separate learning for Reward-Punishment --- \link{prl_fictitious_rp}}
+#'                                                  Fictitious Update with separate learning for Reward-Punishment --- \link{prl_fictitious_rp} \cr
+#'                                                  Fictitious Update with separate learning for Reward-Punishment w/o alpha (indecision point) --- \link{prl_fictitious_rp_woa}}
 #'  \item{\strong{Risk Aversion}}{Prospect Theory (PT) --- \link{ra_prospect} \cr
 #'                                PT without a loss aversion parameter --- \link{ra_noLA} \cr
 #'                                PT without a risk aversion parameter --- \link{ra_noRA}}
@@ -42,14 +44,18 @@
 #'                                       Drift Diffusion Model for single subject --- \link{choiceRT_ddm_single} \cr
 #'                                       Linear Ballistic Accumulator --- \link{choiceRT_lba} \cr
 #'                                       Linear Ballistic Accumulator for single subject --- \link{choiceRT_lba_single}}
+#'  \item{\strong{Two-Step task}}{Full model (7 parameters) --- \link{ts_par7} \cr
+#'                                6 parameter model (without eligibility trace, lambda) --- \link{ts_par6} \cr
+#'                                4 parameter model --- \link{ts_par4}}
+#'                                       
 #' }
 #' 
 #' @seealso 
 #' For tutorials and further readings, visit : \url{http://rpubs.com/CCSL/hBayesDM}.
 #' 
 #' @references 
 #' Please cite as: 
-#' Ahn, W.-Y., Haines, N., & Zhang, L. (2017). Revealing neuro-computational mechanisms of reinforcement learning and decision-making with the hBayesDM package. \emph{Computational Psychiatry}. 1:1. https://doi.org/10.1101/064287
+#' Ahn, W.-Y., Haines, N., & Zhang, L. (2017). Revealing neuro-computational mechanisms of reinforcement learning and decision-making with the hBayesDM package. \emph{Computational Psychiatry}. 1, 24-57. https://doi.org/10.1162/CPSY_a_00002
 #'
 #' @author
 #' Woo-Young Ahn \email{wooyoung.ahn@@gmail.com}

R/prl_ewa.R

@@ -2,6 +2,7 @@
 #'
 #' @description
 #' Hierarchical Bayesian Modeling of the Probabilistic Reversal Learning (PRL) Task using the following parameters: "phi" (1 - learning rate), "rho" (experience decay factor), and "beta" (inverse temperature).
+#' Contributor (model-based regressors): Jaeyeong Yang (https://ccs-lab.github.io/team/jaeyeong-yang/) and Harhim Park (https://ccs-lab.github.io/team/harhim-park/)
 #'
 #' \strong{MODEL:}
 #' Experience-Weighted Attraction Model (Ouden et al., 2013, Neuron)

R/prl_fictitious.R

@@ -2,6 +2,7 @@
 #'
 #' @description
 #' Hierarchical Bayesian Modeling of the Probabilistic Reversal Learning (PRL) Task using the following parameters: "eta" (learning rate), "alpha" (indecision point), "beta" (inverse temperature).
+#' Contributor (for model-based regressors): Jaeyeong Yang (https://ccs-lab.github.io/team/jaeyeong-yang/) and Harhim Park (https://ccs-lab.github.io/team/harhim-park/)
 #'
 #' \strong{MODEL:}
 #' Fictitious Update Model (Glascher et al., 2008, Cerebral Cortex)
@@ -230,7 +231,7 @@ prl_fictitious <- function(data           = "choice",
     genInitList <- "random"
   } else {
     if (inits[1] == "fixed") {
-      inits_fixed <- c(0.5, 0.1, 1.0)
+      inits_fixed <- c(0.5, 0.0, 1.0)
     } else {
       if (length(inits) == numPars)
         inits_fixed <- inits
@@ -240,10 +241,10 @@ prl_fictitious <- function(data           = "choice",
 
     genInitList <- function() {
       list(
-        mu_p     = c(qnorm(inits_fixed[1]), qnorm(inits_fixed[2]), qnorm(inits_fixed[3] / 5)),
+        mu_p     = c(qnorm(inits_fixed[1]), inits_fixed[2], qnorm(inits_fixed[3] / 5)),
         sigma    = c(1.0, 1.0, 1.0),
         eta_pr   = rep(qnorm(inits_fixed[1]), numSubjs),
-        alpha_pr = rep(qnorm(inits_fixed[2]), numSubjs),
+        alpha_pr = rep(inits_fixed[2], numSubjs),
         beta_pr  = rep(qnorm(inits_fixed[3]/5), numSubjs)
       )
     }

R/prl_fictitious_multipleB.R

@@ -2,6 +2,7 @@
 #'
 #' @description
 #' Hierarchical Bayesian Modeling of the Probabilistic Reversal Learning (PRL) Task using the following parameters: "eta" (learning rate), "alpha" (indecision point), "beta" (inverse temperature).
+#' Contributor (for model-based regressors): Jaeyeong Yang (https://ccs-lab.github.io/team/jaeyeong-yang/) and Harhim Park (https://ccs-lab.github.io/team/harhim-park/)
 #'
 #' \strong{MODEL:}
 #' Fictitious Update Model (Glascher et al., 2008, Cerebral Cortex)
@@ -232,7 +233,7 @@ prl_fictitious_multipleB <- function(data           = "choice",
     genInitList <- "random"
   } else {
     if (inits[1] == "fixed") {
-      inits_fixed <- c(0.5, 0.1, 1.0)
+      inits_fixed <- c(0.5, 0.0, 1.0)
     } else {
       if (length(inits) == numPars)
         inits_fixed <- inits
@@ -242,10 +243,10 @@ prl_fictitious_multipleB <- function(data           = "choice",
 
     genInitList <- function() {
       list(
-        mu_p     = c(qnorm(inits_fixed[1]), qnorm(inits_fixed[2]), qnorm(inits_fixed[3] / 10)),
+        mu_p     = c(qnorm(inits_fixed[1]), inits_fixed[2], qnorm(inits_fixed[3] / 10)),
         sigma    = c(1.0, 1.0, 1.0),
         eta_pr   = rep(qnorm(inits_fixed[1]), numSubjs),
-        alpha_pr = rep(qnorm(inits_fixed[2]), numSubjs),
+        alpha_pr = rep(inits_fixed[2], numSubjs),
         beta_pr  = rep(qnorm(inits_fixed[3]/10), numSubjs)
       )
     }

R/prl_fictitious_rp.R

@@ -2,6 +2,7 @@
 #'
 #' @description
 #' Hierarchical Bayesian Modeling of the Probabilistic Reversal Learning (PRL) Task using the following parameters: "eta_pos" (learning rate, +PE), "eta_neg" (learning rate, -PE), "alpha" (indecision point), "beta" (inverse temperature).
+#' Contributor (for model-based regressors): Jaeyeong Yang (https://ccs-lab.github.io/team/jaeyeong-yang/) and Harhim Park (https://ccs-lab.github.io/team/harhim-park/)
 #'
 #' \strong{MODEL:}
 #' Fictitious Update Model (Glascher et al., 2008, Cerebral Cortex) + separate learning rates for + and - prediction error (PE)
@@ -233,7 +234,7 @@ prl_fictitious_rp <- function(data           = "choice",
     genInitList <- "random"
   } else {
     if (inits[1] == "fixed") {
-      inits_fixed <- c(0.5, 0.5, 0.1, 1.0)
+      inits_fixed <- c(0.5, 0.5, 0.0, 1.0)
     } else {
       if (length(inits) == numPars)
         inits_fixed <- inits
@@ -243,11 +244,11 @@ prl_fictitious_rp <- function(data           = "choice",
 
     genInitList <- function() {
       list(
-        mu_p     = c(qnorm(inits_fixed[1]), qnorm(inits_fixed[2]), qnorm(inits_fixed[3]), qnorm(inits_fixed[4] / 5)),
+        mu_p     = c(qnorm(inits_fixed[1]), qnorm(inits_fixed[2]), inits_fixed[3], qnorm(inits_fixed[4] / 5)),
         sigma    = c(1.0, 1.0, 1.0, 1.0),
         eta_pos_pr = rep(qnorm(inits_fixed[1]), numSubjs),
         eta_neg_pr = rep(qnorm(inits_fixed[2]), numSubjs),
-        alpha_pr = rep(qnorm(inits_fixed[3]), numSubjs),
+        alpha_pr = rep(inits_fixed[3], numSubjs),
         beta_pr  = rep(qnorm(inits_fixed[4]/5), numSubjs)
       )
     }