dynConfiR (version 0.0.4)

predictRTConf: Prediction of confidence rating and response time distribution for sequential sampling confidence models

Description

predictConf predicts the categorical response distribution of decision and confidence ratings, predictRT computes the predicted RT distribution (density) for the sequential sampling confidence model specified by the argument model, given specific parameter constellations. This function calls the respective functions for diffusion based models (dynWEV and 2DSD: predictWEV) and race models (IRM, PCRM, IRMt, and PCRMt: predictRM).

Usage

predictConf(paramDf, model = NULL, maxrt = 15, subdivisions = 100L,
  simult_conf = FALSE, stop.on.error = FALSE, .progress = TRUE)
predictRT(paramDf, model = NULL, maxrt = 9, subdivisions = 100L,
  minrt = NULL, simult_conf = FALSE, scaled = FALSE, DistConf = NULL,
  .progress = TRUE)

Value

predictConf returns a data.frame/tibble with columns: condition, stimulus, response, rating, correct, p, info, err. p is the predicted probability of a response and rating, given the stimulus category and condition. info and err refer to the respective outputs of the integration routine used for the computation. predictRT returns a data.frame/tibble with columns: condition, stimulus, response, rating, correct, rt and dens (and densscaled, if scaled=TRUE).

Arguments

paramDf

a list or dataframe with one row. Column names should match the names of the respective model parameters. For different stimulus quality/mean drift rates, names should be v1, v2, v3,.... Different s parameters are possible with s1, s2, s3... with equally many steps as for drift rates (same for sv parameter in dynWEV and 2DSD). Additionally, the confidence thresholds should be given by names with thetaUpper1, thetaUpper2,..., thetaLower1,... or, for symmetric thresholds only by theta1, theta2,....

model

character scalar. One of "2DSD", "dynWEV", "IRM", "PCRM", "IRMt", or "PCRMt".

maxrt

numeric. The maximum RT for the integration/density computation. Default: 15 (for predictConf (integration)), 9 (for predictRT).

subdivisions

integer (default: 100). For predictConf it is used as argument for the inner integral routine. For predictRT it is the number of points for which the density is computed.

simult_conf

logical, only relevant for dynWEV and 2DSD. Whether in the experiment confidence was reported simultaneously with the decision, as then decision and confidence judgment are assumed to have happened subsequent before response and computations are different, when there is an observable interjudgment time (then simult_conf should be FALSE).

stop.on.error

logical. Argument directly passed on to integrate. Default is FALSE, since the densities invoked may lead to slow convergence of the integrals (which are still quite accurate) which causes R to throw an error.

.progress

logical. If TRUE (default) a progress bar is drawn to the console.

minrt

numeric or NULL(default). The minimum rt for the density computation.

scaled

logical. For predictRT. Whether the computed density should be scaled to integrate to one (additional column densscaled). Otherwise the output contains only the defective density (i.e. its integral is equal to the probability of a response and not 1). If TRUE, the argument DistConf should be given, if available. Default: FALSE.

DistConf

NULL or data.frame. A data.frame or matrix with column names, giving the distribution of response and rating choices for different conditions and stimulus categories in the form of the output of predictConf. It is only necessary, if scaled=TRUE, because these probabilities are used for scaling. If scaled=TRUE and DistConf=NULL, it will be computed with the function predictRM_Conf, which takes some time and the function will throw a message. Default: NULL

Author

Sebastian Hellmann.

Details

The function predictConf consists merely of an integration of the reaction time density of the given model, {d*model*}, over the response time in a reasonable interval (0 to maxrt). The function predictRT wraps these density functions to a parameter set input and a data.frame output. For the argument paramDf, the output of the fitting function fitRTConf with the respective model may be used.

Examples

# Examples for "dynWEV" model (equivalent applicable for
# all other models (with different parameters!))

# 1. Define some parameter set in a data.frame
paramDf <- data.frame(a=1.5,v1=0.2, v2=1, t0=0.1,z=0.52,
                      sz=0.3,sv=0.4, st0=0,  tau=3, w=0.5,
                      theta1=1, svis=0.5, sigvis=0.8)

# 2. Predict discrete Choice x Confidence distribution:
preds_Conf <- predictConf(paramDf, "dynWEV", maxrt = 25, simult_conf=TRUE)
head(preds_Conf)


# 3. Compute RT density
preds_RT <- predictRT(paramDf, "dynWEV") #(scaled=FALSE)
# same output with default rt-grid and without scaled density column:
preds_RT <- predictRT(paramDf, "dynWEV", maxrt=5, subdivisions=200,
                      minrt=paramDf$tau+paramDf$t0, simult_conf = TRUE,
                      scaled=TRUE, DistConf = preds_Conf)
head(preds_RT)
# \donttest{
  # produces a warning, if scaled=TRUE and DistConf missing
  preds_RT <- predictRT(paramDf, "dynWEV",
                           scaled=TRUE)
# }

# \donttest{
  # Example of visualization
  library(ggplot2)
  preds_Conf$rating <- factor(preds_Conf$rating, labels=c("unsure", "sure"))
  preds_RT$rating <- factor(preds_RT$rating, labels=c("unsure", "sure"))
  ggplot(preds_Conf, aes(x=interaction(rating, response), y=p))+
    geom_bar(stat="identity")+
    facet_grid(cols=vars(stimulus), rows=vars(condition), labeller = "label_both")
  ggplot(preds_RT, aes(x=rt, color=interaction(rating, response), y=densscaled))+
    geom_line(stat="identity")+
    facet_grid(cols=vars(stimulus), rows=vars(condition), labeller = "label_both")+
    theme(legend.position = "bottom")+ ggtitle("Scaled Densities")
  ggplot(aggregate(dens~rt+correct+rating+condition, preds_RT, mean),
         aes(x=rt, color=rating, y=dens))+
    geom_line(stat="identity")+
    facet_grid(cols=vars(condition), rows=vars(correct), labeller = "label_both")+
    theme(legend.position = "bottom")+ ggtitle("Non-Scaled Densities")
# }
# Use PDFtoQuantiles to get predicted RT quantiles
head(PDFtoQuantiles(preds_RT, scaled = FALSE))