From 5b7cd0e2dc648391eef90266e3e9e7a958bc1273 Mon Sep 17 00:00:00 2001 From: Rausch Date: Thu, 31 Oct 2024 17:56:56 +0100 Subject: [PATCH] create plotting function --- R/fitConfModels.R | 9 ++- R/plotConfModelFit.R | 143 +++++++++++++++++++++++++++++++++++++++++++ README.rmd | 11 ++-- TestScript.R | 33 ++++++---- data/MaskOri.RData | Bin 46979 -> 43754 bytes 5 files changed, 175 insertions(+), 21 deletions(-) create mode 100644 R/plotConfModelFit.R diff --git a/R/fitConfModels.R b/R/fitConfModels.R index b74db52..160bfd1 100644 --- a/R/fitConfModels.R +++ b/R/fitConfModels.R @@ -42,11 +42,10 @@ #' @param n.cores `integer`. Number of cores used for parallelization. If NULL (default), the available #' number of cores -1 will be used. -#' @return Gives data frame with one row for each combination of model and -#' participant. Columns include a model and participant column, -#' one column for each estimated parameter for the different models (parameters -#' that are not present in a specific model (row) but in other models are -#' filled with NAs. +#' @return Gives `data.frame` with one row for each combination of model and +#' participant. There are different columns for the model, the participant ID, and one +#' one column for each estimated model parameter (parameters +#' not present in a specific model are filled with NAs). #' Additional information about the fit is provided in additional columns: #' - `negLogLik` (negative log-likelihood of the best-fitting set of parameters), #' - `k` (number of parameters), diff --git a/R/plotConfModelFit.R b/R/plotConfModelFit.R new file mode 100644 index 0000000..b26f5ea --- /dev/null +++ b/R/plotConfModelFit.R @@ -0,0 +1,143 @@ +#' @title Plot the prediction of fitted parameters of one model of confidence over the corresponding real data +#' +#' @description The `plotConfModelFit` function plots the predicted distribution of discrimination responses +#' and confidence ratings created from a `data.frame` of parameters obtaind from \code{\link{fitConfModels}} +#' and overlays the predicted distribution over the data to which the model parameters were fitted. +#' +#' @param data a `data.frame` where each row is one trial, containing following +#' variables: +#' * \code{diffCond} (optional; different levels of discriminability, +#' should be a factor with levels ordered from hardest to easiest), +#' * \code{rating} (discrete confidence judgments, should be a factor with levels +#' ordered from lowest confidence to highest confidence; +#' otherwise will be transformed to factor with a warning), +#' * \code{stimulus} (stimulus category in a binary choice task, +#' should be a factor with two levels, otherwise it will be transformed to +#' a factor with a warning), +#' * \code{correct} (encoding whether the response was correct; should be 0 for +#' incorrect responses and 1 for correct responses) +#' * \code{participant} (some group ID, most often a participant identifier; +#' the models given in the second argument are fitted to each subset of `data` +#' determined by the different values of this column) +#' +#' @param fitted_pars a `data.frame` with one row for each participant and model parameters in different columns. +#' fitted_pars also may contain a column called `model`specifying the model to be visualized. +#' If there is no model column in data or if there are multiple models in fitted_pars, +#' it is necessary to specify the model argument. +#' +#' @param model `character`. See \code{\link{fitConf}} for all available models +#' +#' @return a `data.frame` with one row for each combination of model and +#' participant. There are different columns for the model, the participant ID, and one +#' one column for each estimated model parameter (parameters +#' not present in a specific model are filled with NAs) +#' +#' #' @examples +#' # 1. Select two subjects from the masked orientation discrimination experiment +#' data <- subset(MaskOri, participant %in% c(1:2)) +#' head(data) +#' +#' # 2. Fit some models to each subject of the masked orientation discrimination experiment +#' \donttest{ +#' # Fitting several models to several subjects takes quite some time +#' # (about 10 minutes per model fit per participant on a 2.8GHz processor +#' # with the default values of nInits and nRestart). +#' # If you want to fit more than just two subjects, +#' # we strongly recommend setting .parallel=TRUE +#' Fits <- fitConfModels(data, models = "ITGc", .parallel = FALSE) +#' } +#' # 3. Plot the predicted probabilies based on model and fitted parameter over the observed relative frequencies. +#' +#' \donttest{ +#' # Fitting several models to several subjects takes quite some time +#' # (about 10 minutes per model fit per participant on a 2.8GHz processor +#' # with the default values of nInits and nRestart). +#' # If you want to fit more than just two subjects, +#' # we strongly recommend setting .parallel=TRUE +#' myPlottedFit <- plotConfModelFit(data, Fits) +#' myPlottedFit +#' } +#' @import ggplot2 +#' @importFrom plyr ddply transform summarise +#' @importFrom Rmisc summarySEwithin +#' +#' @export +plotConfModelFit <- function(data, fitted_pars, model = NULL){ + if(is.null(model)){ + if("model" %in% colnames(fitted_pars)){ + if(length(unique(fitted_pars$model))==1){ + model <- unique(fitted_pars$model) + } else { + stop("Please use the model argument to specify which model should be used") + } + }else { + stop("Please specify which model should be used") + } + } + if (is.null(data$diffCond)) data$diffCond <- factor(1) + if (!is.factor(data$diffCond)) { + data$diffCond <- factor(data$diffCond) + } + if(length(unique(data$stimulus)) != 2) { + stop("There must be exacltly two different possible values of stimulus") + } + + if (!is.factor(data$stimulus)) { + data$stimulus <- factor(data$stimulus) + } + if (!is.factor(data$rating)) { + data$rating <- factor(data$rating) + } + if(!all(data$correct %in% c(0,1))) stop("correct should be 1 or 0") + + myColor <- switch(model, 'GN' = 1, 'IG' = 2, 'ITGc' = 3, 'ITGcm' = 4, 'logN' = 5, + 'logWEV' = 6,'PDA' = 7, 'WEV' = 8, 'SDT' = 9) # models are color coded + + # 1. First aggregate on the level of subjects + + AggDist <- + plyr::ddply(data, + ~ diffCond * rating * stimulus * correct * participant, #, + plyr::summarise, p = length(rating), .drop=FALSE) + + AggDist <- plyr::ddply(AggDist, ~ diffCond * stimulus, + transform, N = sum(p)) + AggDist$p <- AggDist$p / AggDist$N + + + AggDist$rating <- as.numeric(AggDist$rating) + AggDist$correct <- + factor(AggDist$correct) + levels(AggDist$correct) <- + c("incorrect", "correct") + + # 2. aggregate across subjects + AggDist <- + Rmisc::summarySEwithin(AggDist , measurevar = "p", + withinvars = c("diffCond", "correct", "rating", "stimulus"), + idvar = "participant", + na.rm = TRUE, .drop = TRUE) + AggDist$rating <- as.numeric(AggDist$rating) + levels(AggDist$stimulus) <- c("S = -1", "S = 1") + levels(AggDist$diffCond) <- paste("K =", as.numeric(levels(AggDist$diffCond))) + + # 3) create a plot with the observed data + PlotObsVsPred <- + ggplot(AggDist, + aes(x=rating, y = p)) + + facet_grid(diffCond ~ stimulus+correct) + + geom_bar(stat="identity", + fill ="white", color = "black") + + geom_errorbar(aes(ymin=p-se, ymax=p+se), width=0) + + xlab("Confidence rating") + + ylab("probability") + + theme(strip.text.y = element_text(angle=0)) + + theme_minimal() + + + # create a plot from the observed data + + + PlotObsVsPred +} + diff --git a/README.rmd b/README.rmd index e211685..19ce851 100644 --- a/README.rmd +++ b/README.rmd @@ -1,6 +1,6 @@ --- title: "README" -author: "Manuel Rausch, >Sascha Meyen, and Sebastian Hellmann" +author: "Manuel Rausch, Sascha Meyen, and Sebastian Hellmann" date: "2024-10-13" output: md_document: @@ -190,10 +190,10 @@ $$RMI = \frac{meta-I}{\max_{\text{accuracy}}\{meta-I\}}$$ As these measures are prone to estimation bias, the package offers a simple bias reduction mechanism in which the observed frequencies of -stimulus-response combinations are taken as the underyling probability +stimulus-response combinations are taken as the underlying probability distribution. From this, Monte-Carlo simulations are conducted to estimate -and subtract the bias in these measures. Note that there provably is no way -to completely remove this bias. +and subtract the bias from these measures. Note that there is probably no way +to remove this bias completely. ## Installation @@ -324,6 +324,7 @@ the data (notably, this is not the case though in the demo data set), the functi ```{r} MetaDs <- fitMetaDprime(data = MaskOri, model="ML", .parallel = TRUE) +head(MetaDs) ``` Information theoretic measures of metacognition can be obtained by the function `estimateMetaI`. It expects the same kind of data.frame as `fitMetaDprime` @@ -332,7 +333,7 @@ The preferred way to estimate these measures is with bias reduction, but this ma ```{r} metaIMeasures <- estimateMetaI(data = MaskOri, bias_reduction = TRUE) -metaIMeasures +head(metaIMeasures) ``` For the impatient and for testing purposes, bias_reduction can be turned off to increase computation speed: diff --git a/TestScript.R b/TestScript.R index d66f1e9..8ca9f17 100644 --- a/TestScript.R +++ b/TestScript.R @@ -356,18 +356,28 @@ Plot_recov_metaDprime_F # 4. meta-I and co -MetaInfoMeasures <- - MaskOri %>% - mutate(y = fct_recode(stimulus, "-1" = "0", "1" = "90")) %>% - mutate(r = factor(ifelse(response=="0", -1, 1) * as.numeric(rating))) %>% - group_by(participant) %>% #group_modify(~ estimate_meta_I(.x)) - dplyr::summarise(meta_I = estimate_meta_I(pick(y,r)), - meta_Ir1 = estimate_meta_Ir1(pick(y,r)), - meta_Ir1_acc = estimate_meta_Ir1_acc(pick(y,r)), - meta_Ir2 = estimate_meta_Ir2(pick(y,r)), - RMI = estimate_RMI(pick(y,r)) - ) # need dplyr::summarise because plyr::summarise is used somewhere else +MetaDs <- fitMetaDprime(data = MaskOri, model="ML", .parallel = TRUE) +MetaInfoMeasures <- estimateMetaI(data = MaskOri, bias_reduction = F) + + +merge(MetaDs %>% select(participant, Ratio), + MetaInfoMeasures %>% select(participant, meta_Ir1)) %>% + ggplot(aes(x=Ratio, y=meta_Ir1)) + + geom_point() + geom_smooth(method="lm", se=F)+ + theme_minimal() + + +# 5) Plotting fits +PlotFitSDT <- plotConfModelFit(MaskOri, fitted_pars, model="SDT") +PlotFitGN <- plotConfModelFit(MaskOri, fitted_pars, model="GN") +PlotFitLogN <- plotConfModelFit(MaskOri, fitted_pars, model="logN") +PlotFitWEV <- plotConfModelFit(MaskOri, fitted_pars, model="WEV") +PlotFitLogWEV <- plotConfModelFit(MaskOri, fitted_pars, model="logWEV") +PlotFitITGcm <- plotConfModelFit(MaskOri, fitted_pars, model="ITGcm") +PlotFitITGc <- plotConfModelFit(MaskOri, fitted_pars, model="ITGc") +PlotFitIG <- plotConfModelFit(MaskOri, fitted_pars, model="IG") +PlotFitPDA <- plotConfModelFit(MaskOri, fitted_pars, model="PDA") save(fitted_pars, PlotFitsBICWeights, @@ -385,3 +395,4 @@ save(fitted_pars, PlotFitsBICWeights, recov_metaDprime_F, Plot_recov_metaDprime_F, MetaInfoMeasures, file = "TestResults.RData") +PlotFitSDT <- plotConfModelFit(MaskOri, fitatted_pars, model="SDT") diff --git a/data/MaskOri.RData b/data/MaskOri.RData index 799659a8b7094657a98d4c9d8627405bc0597d93..72a1951d4e404143aed72d23848ab11857f973bc 100644 GIT binary patch literal 43754 zcmbrl`(KjjxS-RmIpLXP!TFBk|?5rAfh6I@L}ygVJ%)S9)96@?)$m! z>%Ok{^}gP>XT|^d|Ni&$(S{X2b5HTJ;?M@U^4-OYp9J3e+kg8FpGXS2F#0t4=g-q4 z^8PmSZ*9KK8Pcv7$2;Q#PgV!i9`VvpbB~8OFEB6cTy~+xCJ}__Sz7Jh7hia!v zrt5gA5B*tp4LCi+=zXxOv}sGM9712>v^HEay)!g2ZNeT2hngf9A)9E_3TGrHAJ|KW?J($+ZrkKg|Hp-cW` zpS{pIpELdsUu-|xjQ{Y>|2wer2@n3MDa>!VZ5@^W6Yt=hM${{4-al@HzrP3c3)0{} zy%@LuVQ2VXN3tBYlxF@jF4e8}UiRev6F%u2obeaZ@;_~e@BV2+T=}OB5taVWnlkF2 z+TY!3>()+B$$@q!01EJ|?1H`e${uc%E0#2at6|mFcN@E=Ut7i+);`nVQ#&gp$&o9P 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