Changed email addresses, bug fixes

Author: ManuelRausch

R/fitConfModels.R

@@ -202,7 +202,7 @@
 #' each belonging to one possible identity of the stimulus.
 #' Both samples of sensory evidence \eqn{x_{-1}} and
 #' \eqn{x_1} are sampled from Gaussian distributions with a standard deviations of \eqn{\sqrt{1/2}}.
-#' The mean of \eqn{x_{-1}} is given by \eqn{(1 − S) \times 0.25 \times d}; the mean
+#' The mean of \eqn{x_{-1}} is given by \eqn{(1 - S) \times 0.25 \times d}; the mean
 #' of \eqn{x_1} is given by \eqn{(1 + S) \times 0.25 \times d}. The sensory evidence
 #' used for the discrimination choice is \eqn{x = x_2 - x_1},
 #' which implies that the discrimination decision is equivalent to standard SDT.

R/groupBMS.R

@@ -48,7 +48,7 @@
 #' It therefore aggregates the available subject-level model evidences
 #' according to a random effects model, which assumes that model probabilities
 #' are distributed in the population according to a Dirichlet distribution.
-#' The parameters of the Dirichelet distribution are determined using a variational Bayes algorithm.
+#' The parameters of the Dirichlet distribution are determined using a variational Bayes algorithm.
 #' `groupBMS` provides three statistics associated with the random effects model:
 #' * estimated model frequencies in the population,
 #' * The exceedance probability  represents the probability that a specific
@@ -64,7 +64,7 @@
 #' @references Stephan, K. E., Penny, W. D., Daunizeau, J., Moran, R. J., & Friston, K. J. (2009). Bayesian model selection for group studies. Neuroimage, 46(4), 1004-1017.
 
 #' @author
-#' Sebastian Hellmann, \email{sebastian.hellmann@@ku.de}\cr
+#' Sebastian Hellmann, \email{sebastian.hellmann@tum.de}\cr
 #' Manuel Rausch, \email{manuel.rausch@ku.de}
 #'
 #' @examples
@@ -83,7 +83,7 @@
 #'
 #' @importFrom stats rgamma
 #' @export
-groupBMS <- function(fits, measure = "BIC", opts=list()) {
+groupBMS <- function(fits, measure = "AICc", opts=list()) {
   if (!measure %in% c("BIC", "AIC", "AICc"))
     stop(paste0("measure=", measure, " should be BIC, AIC, or AICc"))
   models <- sort(unique(fits$model))

man/fitConfModels.Rd

@@ -1,21 +1,21 @@
 Package: statConfR
 Type: Package
 Title: Models of Decision Confidence and Measures of Metacognition
-Version: 0.2.0
-Date: 2025-02-11
+Version: 0.2.1
+Date: 2025-02-22
 Authors@R: c(
-  person("Manuel", "Rausch", email = "manuel.rausch@hochschule-rhein-waal.de", 
+  person("Manuel", "Rausch", email = "manuel.rausch@ku.de", 
   role = c("aut", "cre"), comment = c(ORCID = "0000-0002-5805-5544")),
     person("Sascha", "Meyen", email = "saschameyen@gmail.com", 
     role = c("aut"), comment = c(ORCID = "0000-0001-6928-4126")),
     person("Sebastian", "Hellmann", email = "sebastian.hellmann@tum.de", 
     role = c("aut"), comment = c(ORCID = "0000-0002-3621-6343"))
        )
-Maintainer: Manuel Rausch <manuel.rausch@hochschule-rhein-waal.de>
+Maintainer: Manuel Rausch <manuel.rausch@ku.de>
 Description: Provides fitting functions and other tools for decision confidence 
     and metacognition researchers, including meta-d'/d', often considered to be 
     the gold standard to measure metacognitive efficiency, and information-theoretic measures of metacognition. 
-    Also allows to fit several static models of decision making and confidence.
+    Also allows to fit and compare several static models of decision making and confidence.
 License: GPL(>=3)
 URL: https://github.com/ManuelRausch/StatConfR
 BugReports: https://github.com/ManuelRausch/StatConfR/issues
@@ -25,7 +25,7 @@ Date/Publication: 2023-09-12
 Encoding: UTF-8
 LazyData: true
 NeedsCompilation: no
-Packaged: 2025-02-13 13:50:54 UTC; PPA714
+Packaged: 2025-07-28 07:51:45 UTC; PPA714
 Repository: CRAN
 Roxygen: list(old_usage = TRUE, markdown=TRUE)
 RoxygenNote: 7.3.2

man/groupBMS.Rd

@@ -4,6 +4,7 @@ export(estimateMetaI)
 export(fitConf)
 export(fitConfModels)
 export(fitMetaDprime)
+export(groupBMS)
 export(plotConfModelFit)
 export(simConf)
 import(ggplot2)
@@ -19,5 +20,6 @@ importFrom(stats,optim)
 importFrom(stats,plnorm)
 importFrom(stats,pnorm)
 importFrom(stats,qnorm)
+importFrom(stats,rgamma)
 importFrom(stats,rmultinom)
 importFrom(utils,tail)

statConfR.Rcheck/00_pkg_src/statConfR/DESCRIPTION

@@ -170,7 +170,7 @@
 #' addition to the set of shared parameters.
 
 #' @author Sebastian Hellmann, \email{sebastian.hellmann@tum.de}\cr
-#' Manuel Rausch, \email{manuel.rausch@hochschule-rhein-waal.de}
+#' Manuel Rausch, \email{manuel.rausch@ku.de}
 
 # unlike for the other tags, the references are formatted more nicely if each reference is tagged seperately
 #' @references Akaike, H. (1974). A New Look at the Statistical Model Identification. IEEE Transactions on Automatic Control, AC-19(6), 716–723.doi: 10.1007/978-1-4612-1694-0_16\cr
@@ -186,6 +186,7 @@
 #' @references Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461–464. doi: 10.1214/aos/1176344136\cr
 #' @references Shekhar, M., & Rahnev, D. (2021). The Nature of Metacognitive Inefficiency in Perceptual Decision Making. Psychological Review, 128(1), 45–70. doi: 10.1037/rev0000249\cr
 #' @references Shekhar, M., & Rahnev, D. (2023). How Do Humans Give Confidence? A Comprehensive Comparison of Process Models of Perceptual Metacognition. Journal of Experimental Psychology: General. doi:10.1037/xge0001524\cr
+#' @references Peters, M. A. K., Thesen, T., Ko, Y. D., Maniscalco, B., Carlson, C., Davidson, M., Doyle, W., Kuzniecky, R., Devinsky, O., Halgren, E., & Lau, H. (2017). Perceptual confidence neglects decision-incongruent evidence in the brain. Nature Human Behaviour, 1(0139), 1–21. doi:10.1038/s41562-017-0139
 
 #' @examples
 #' # 1. Select one subject from the masked orientation discrimination experiment
@@ -264,7 +265,9 @@ fitConf <- function(data, model = "SDT",
     fitting_fct <- fitLognorm
   } else if (model == "logWEV"){
     fitting_fct <- fitLogWEV
-  } else stop(paste0("Model: ", model, " not implemented!\nChoose one of: 'WEV', 'SDT', 'IG', 'ITGc', 'ITGcm, 'GN', 'logN', 'logWEV', or 'PDA'"))
+  } else if (model == "RCE"){
+    fitting_fct <- fitRCE
+  } else stop(paste0("Model: ", model, " not implemented!\nChoose one of: 'WEV', 'SDT', 'IG', 'ITGc', 'ITGcm, 'GN', 'logN', 'logWEV', 'RCE', or 'PDA'"))
 
   fit <- fitting_fct(N_SA_RA = N_SA_RA,N_SA_RB = N_SA_RB,
                      N_SB_RA = N_SB_RA,N_SB_RB = N_SB_RB,

statConfR.Rcheck/00_pkg_src/statConfR/NAMESPACE

@@ -30,7 +30,7 @@
 #'    the models given in the second argument are fitted to each subset of `data`
 #'    determined by the different values of this column)
 #' @param models `character`. The different computational models that should be
-#'    fitted. Models implemented so far: 'WEV', 'SDT', 'GN', 'PDA', 'IG', 'ITGc',
+#'    fitted. Models implemented so far: 'WEV', 'SDT', 'GN', 'PDA', 'IG', 'ITGc', 'RCE',
 #'    'ITGcm', 'logN', and 'logWEV'. Alternatively, if `model="all"` (default),
 #'    all implemented models will be fit.
 #' @param nInits `integer`. Number of initial values used for maximum likelihood optimization.
@@ -193,10 +193,25 @@
 #' The parameter \eqn{w} represents the weight that is put on the choice-irrelevant
 #' features in the confidence judgment. \eqn{w} and \eqn{\sigma} are fitted in
 #' addition to the set of shared parameters.
+#'
+#' ### \strong{Response-congruent evidence model (RCE)}
+#' The response-congruent evidence model represents the idea that observers use
+#' all available sensory information to make the discrimination decision, but for confidence judgements,
+#' they only consider evidence consistent with the selected decision and ignore evidence against the decision (Peters et al., 2017).
+#' The model assumes two separate samples of sensory evidence collected in each trial,
+#' each belonging to one possible identity of the stimulus.
+#' Both samples of sensory evidence \eqn{x_{-1}} and
+#' \eqn{x_1} are sampled from Gaussian distributions with a standard deviations of \eqn{\sqrt{1/2}}.
+#' The mean of \eqn{x_{-1}} is given by \eqn{(1 − S) \times 0.25 \times d}; the mean
+#' of \eqn{x_1} is given by \eqn{(1 + S) \times 0.25 \times d}. The sensory evidence
+#' used for the discrimination choice is \eqn{x = x_2 - x_1},
+#' which implies that the discrimination decision is equivalent to standard SDT.
+#' The confidence decision variable y is \eqn{y = - x_1} if the response R is -1 and \eqn{y = x_2} otherwise.
+
 
 #' @author
 #' Sebastian Hellmann, \email{sebastian.hellmann@tum.de}\cr
-#' Manuel Rausch, \email{manuel.rausch@hochschule-rhein-waal.de}
+#' Manuel Rausch, \email{manuel.rausch@ku.de}
 
 # unlike for the other tags, the references are formatted more nicely if each reference is tagged seperately
 #' @references Akaike, H. (1974). A New Look at the Statistical Model Identification. IEEE Transactions on Automatic Control, AC-19(6), 716–723.doi: 10.1007/978-1-4612-1694-0_16\cr
@@ -212,6 +227,8 @@
 #' @references Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461–464. doi: 10.1214/aos/1176344136\cr
 #' @references Shekhar, M., & Rahnev, D. (2021). The Nature of Metacognitive Inefficiency in Perceptual Decision Making. Psychological Review, 128(1), 45–70. doi: 10.1037/rev0000249\cr
 #' @references Shekhar, M., & Rahnev, D. (2023). How Do Humans Give Confidence? A Comprehensive Comparison of Process Models of Perceptual Metacognition. Journal of Experimental Psychology: General. doi:10.1037/xge0001524\cr
+#' @references Peters, M. A. K., Thesen, T., Ko, Y. D., Maniscalco, B., Carlson, C., Davidson, M., Doyle, W., Kuzniecky, R., Devinsky, O., Halgren, E., & Lau, H. (2017). Perceptual confidence neglects decision-incongruent evidence in the brain. Nature Human Behaviour, 1(0139), 1–21. doi:10.1038/s41562-017-0139
+
 
 #' @examples
 #' # 1. Select two subjects from the masked orientation discrimination experiment
@@ -237,7 +254,7 @@ fitConfModels <- function(data, models="all",
                           nInits = 5, nRestart = 4,
                           .parallel=FALSE, n.cores=NULL) {
   AllModels <- c('WEV', 'SDT', 'IG', 'ITGc',
-                 'ITGcm', 'GN', 'PDA', 'logN', 'logWEV') # if you implement additional models, add them here!
+                 'ITGcm', 'GN', 'PDA', 'logN', 'logWEV', 'RCE') # if you implement additional models, add them here!
   if (identical(models,"all")) models <- AllModels
   if (!all(models %in% AllModels)) {
     stop(paste(paste(setdiff(models, AllModels),collapse = " and "), " not implemented!"))

statConfR.Rcheck/00_pkg_src/statConfR/R/fitConf.R

@@ -1,5 +1,6 @@
 ###   Functions to fit the WEV model
 ### Model version described by (Rausch et al., 2023)
+
 fitCEV <-
   function(N_SA_RA, N_SA_RB, N_SB_RA, N_SB_RB,
            nInits, nRestart, nRatings, nCond, nTrials){

statConfR.Rcheck/00_pkg_src/statConfR/R/fitConfModels.R

@@ -83,7 +83,7 @@
 #' @importFrom Rmisc summarySEwithin
 #' @importFrom stats aggregate
 
-#' @author Manuel Rausch, \email{manuel.rausch@hochschule-rhein-waal.de}
+#' @author Manuel Rausch, \email{manuel.rausch@ku.de}
 
 #' @export
 plotConfModelFit <- function(data, fitted_pars, model = NULL){