more stuff

R/fitConf.R

@@ -196,9 +196,14 @@
 #'
 
 #' @export
-fitConf <- function(data, model = "SDT", nInits = 5,
-                    nRestart = 4#, var="constant"
+fitConf <- function(data, model = "SDT",
+                    condition = NULL, stimulus = NULL, correct = NULL, rating = NULL,
+                    nInits = 5, nRestart = 4,
 ) {
+  if (!is.null(condition)) data$condition <- data[,condition]
+  if (!is.null(stimulus)) data$stimulus <- data[,stimulus]
+  if (!is.null(correct)) data$correct <- data[,correct]
+  if (!is.null(rating)) data$rating <- data[,rating]
   if (is.null(data$condition)) data$condition <- 1
   if (!is.factor(data$condition)) {
     data$condition <- factor(data$condition)

R/fitConfModels.R

@@ -224,6 +224,10 @@ fitConfModels <- function(data, models="all",  nInits = 5, nRestart = 4, #var="c
     warning("Duplicate models are dropped")
     models <- unique(models)
   }
+  if (!is.null(condition)) data$condition <- data[,condition]
+  if (!is.null(stimulus)) data$stimulus <- data[,stimulus]
+  if (!is.null(correct)) data$correct <- data[,correct]
+  if (!is.null(rating)) data$rating <- data[,rating]
   if (is.null(data$condition)) data$condition <- factor(1)
   if (!is.factor(data$condition)) {
     data$condition <- factor(data$condition)

R/int_fitMetaDprime.R

@@ -71,7 +71,7 @@ int_fitMetaDprime   <- function(ratings, stimulus, correct,
       m <- try(optim(par = inits[j,], fn = negLoglMetaD, gr = NULL,
                      nC_rS1 = nC_rS1, nI_rS1 = nI_rS1, nC_rS2 = nC_rS2, nI_rS2 = nI_rS2,
                      nRatings = nRatings, cprime = cprime,
-                     control = list(maxit = 10^6, reltol = 10^-8)), silent=T)
+                     control = list(maxit = 10^4, reltol = 10^-4)), silent=T)
       for(i in 2:nRestart){
         try(m <- try(optim(par = m$par, fn = negLoglMetaD, gr = NULL,
                            nC_rS1 = nC_rS1, nI_rS1 = nI_rS1, nC_rS2 = nC_rS2, nI_rS2 = nI_rS2,
@@ -118,7 +118,8 @@ int_fitMetaDprime   <- function(ratings, stimulus, correct,
     dprime = dprime,
     cprime = cprime,
     c = dprime * cprime,
-    metaD = NA, Ratio = NA, ModelVersion = ModelVersion)
+    metaD = NA, Ratio = NA,
+    ModelVersion = ModelVersion)
 
   if(exists("fit")){
     if(is.list(fit)){

README.md

@@ -1,25 +1,32 @@
 # statConfR: Models of Decision Confidence and Metacognition
 
-This package includes functions to fit meta-d´/d´ as well as several
-static models of decision making and confidence derived from signal
-detection theory to test the assumptions underlying meta-d´/d´ and which
-may serve as an alternative when the assumptions of meta-d´/d´ do not
-hold. Currently, the following models are included:
+The statConfR package provides functions to fit static models of
+decision-making and confidence derived from signal detection theory for
+binary discrimination tasks, as well as meta-d′/d′ (Rausch & Hellmann,
+2024). The package can be used to test the assumptions underlying
+meta-d′/d′. Several models provide a metacognition parameter that may
+serve as an alternative when the assumptions of meta-d′/d′ assuming the
+corresponding model provides a better fit to the data. Currently, the
+following models are included:
 
-- Signal Detection Rating Model
+- Signal detection rating model
 
-- Gaussian Noise Model
+- Gaussian noise model
 
-- Weighted Evidence and Visibility Model
+- Weighted evidence and visibility model
 
 - Post-decisional Accumulation Model
 
 - Independent Gaussian model
 
-- Independent Truncated Gaussian Model (the model underlying the
-  meta-d´/d´ method)
+- Independent truncated gaussian Model (the model underlying the
+  meta-d′/d′ method, see Rausch et al., 2023)
 
-See Rausch et al. (2023) for more detail about the models.
+- Lognormal noise model
+
+- Lognormal weighted evidence and visibility model
+
+- Response-congruent evidence model
 
 ## Installation
 
@@ -131,19 +138,12 @@ head(fitted_pars)
     ## 6 -1.2341607 -0.07619421  0.2861275 1.2793275 2.018620
 
 If the Truncated Gaussian model performs best in the model comparison,
-metacognitive efficiency can be quantified using meta-d’/d’:
+metacognitive efficiency can be quantified using meta-d′/d′:
 
 ``` r
  MetaDs <- fitMetaDprime(MaskOri, model="ML", .parallel = TRUE)
 ```
 
-## Future development
-
-The package is still under active development. We plan to include more
-models from the literature and function to compute the predicted
-distribution of responses for given parameter sets. For any suggestions
-and contributions, please contact us!
-
 ## Contact
 
 For comments, remarks, and questions please contact either
@@ -154,4 +154,8 @@ or [submit an issue](https://github.com/ManuelRausch/StatConfR/issues).
 
 Rausch, M., Hellmann, S. & Zehetleitner, M. (2023). Measures of
 metacognitive efficiency across cognitive models of decision confidence.
-[doi: 10.31234/osf.io/kdz34](https://doi.org/10.31234/osf.io/kdz34)
+Psychological Methods.
+[doi:10.1037/met0000634](https://doi.org/10.1037/met0000634) Rausch, M.,
+& Hellmann, S. (2024). statConfR: An R Package for Static Models of
+Decision Confidence and Metacognition. PsyArXiv.
+[doi:10.31234/osf.io/dk6mr](https://doi.org/10.31234/osf.io/dk6mr)

README.rmd

@@ -1,7 +1,7 @@
 ---
 title: "README"
 author: "Sebastian Hellmann and Manuel Rausch"
-date: "2023-09-15"
+date: "2024-04-10"
 output:
   md_document:
     variant: gfm
@@ -18,9 +18,14 @@ gitbranch <- "main/"
 
 # statConfR: Models of Decision Confidence and Metacognition
 
-This package includes functions to fit meta-d´/d´ as well as several
-static models of decision making and confidence derived from signal detection theory. It can be used to test the
-assumptions underlying meta-d´/d´ and which may serve as an alternative when the assumptions of meta-d´/d´ do not hold. Currently, the following models are included:
+The statConfR package provides functions to fit static models of
+decision-making and confidence derived from signal detection theory for
+binary discrimination tasks, as well as meta-d′/d′ (Rausch & Hellmann,
+2024). The package can be used to test the assumptions underlying
+meta-d′/d′. Several models provide a metacognition parameter that may
+serve as an alternative when the assumptions of meta-d′/d′ assuming the
+corresponding model provides a better fit to the data. Currently, the
+following models are included:
 
 -   Signal detection rating model
 
@@ -33,13 +38,13 @@ assumptions underlying meta-d´/d´ and which may serve as an alternative when t
 -   Independent Gaussian model
 
 -   Independent truncated gaussian Model (the model underlying the
-    meta-d´/d´ method)
-    
+    meta-d′/d′ method, see Rausch et al., 2023)
+
 -   Lognormal noise model
 
 -   Lognormal weighted evidence and visibility model
 
-See Rausch et al. (2023) for more detail about the models.
+-   Response-congruent evidence model
 
 ## Installation
 
@@ -443,19 +448,12 @@ head(fitted_pars)
 ```
 
 If the Truncated Gaussian model performs best in the model comparison,
-metacognitive efficiency can be quantified using meta-d'/d':
+metacognitive efficiency can be quantified using meta-d′/d′:
 
 ``` r
  MetaDs <- fitMetaDprime(MaskOri, model="ML", .parallel = TRUE)
 ```
 
-## Future development
-
-The package is still under active development. We plan to include more
-models from the literature and function to compute the predicted
-distribution of responses for given parameter sets. For any suggestions
-and contributions, please contact us!
-
 ## Contact
 
 For comments, remarks, and questions please contact either
@@ -467,4 +465,9 @@ or [submit an issue](https://github.com/ManuelRausch/StatConfR/issues).
 
 Rausch, M., Hellmann, S. & Zehetleitner, M. (2023). Measures of
 metacognitive efficiency across cognitive models of decision confidence.
-[doi: 10.31234/osf.io/kdz34](https://doi.org/10.31234/osf.io/kdz34)
+Psychological Methods.
+[doi:10.1037/met0000634](https://doi.org/10.1037/met0000634)
+
+Rausch, M., & Hellmann, S. (2024). statConfR: An R Package for Static
+Models of Decision Confidence and Metacognition. PsyArXiv.
+[doi:10.31234/osf.io/dk6mr](https://doi.org/10.31234/osf.io/dk6mr)