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#' A3 Results for Arbitrary Model
#'
#' This function calculates the A3 results for an arbitrary model construction algorithm (e.g. Linear Regressions, Support Vector Machines or Random Forests). For linear regression models, you may use the \code{\link{a3.lm}} convenience function.
#'
#' @param formula the regression... | /scratch/gouwar.j/cran-all/cranData/A3/R/A3.R |
#' Boston Housing Prices
#'
#' A dataset containing the prices of houses in the Boston region and a number of features.
#' The dataset and the following description is based on that provided by UCI Machine Learning Repository (\url{http://archive.ics.uci.edu/ml/datasets/Housing}).
#'
#' \itemize{
#' \item CRIME: Pe... | /scratch/gouwar.j/cran-all/cranData/A3/R/A3.data.R |
#############################
# bootstrapped bias score computation
#' @title Compute bootstrapped approach-bias scores
#' @description Compute bootstrapped approach-bias scores with confidence intervals.
#' @param ds a longformat data.frame
#' @param subjvar Quoted name of the participant identifier column
#' @param p... | /scratch/gouwar.j/cran-all/cranData/AATtools/R/aat_bootstrap.R |
#' @title Compute simple AAT scores
#' @description Compute simple AAT scores, with optional outlier exclusion and error trial recoding.
#' @param ds a long-format data.frame
#' @param subjvar column name of subject variable
#' @param pullvar column name of pull/push indicator variable, must be numeric or logical (whe... | /scratch/gouwar.j/cran-all/cranData/AATtools/R/aat_compute.R |
#' Compute stimulus-specific bias scores
#' Computes mean single-difference scores (push - pull) for each stimulus.
#'
#' @param ds the \code{data.frame} to use
#' @param subjvar Name of the subject-identifying variable
#' @param stimvar Name of the stimulus-identifying variable
#' @param pullvar Name of the movement... | /scratch/gouwar.j/cran-all/cranData/AATtools/R/aat_covreliability.R |
#' Simulate AAT datasets and predict parameters
#'
#' \code{aat_simulate()} generates approach-avoidance task datasets.
#'
#' @param npps Number of participants
#' @param nstims Number of stimuli
#' @param stimreps Number of repetitions of each stimulus within each group
#' (i.e. within approach target, avoid target, a... | /scratch/gouwar.j/cran-all/cranData/AATtools/R/aat_simulate.R |
# splithalf engine ####
#multicore splithalf
#' @title Compute the bootstrapped split-half reliability for approach-avoidance task data
#' @description Compute bootstrapped split-half reliability for approach-avoidance task data.
#' @param ds a longformat data.frame
#' @param subjvar Quoted name of the participant ide... | /scratch/gouwar.j/cran-all/cranData/AATtools/R/aat_splithalf.R |
subtraction.matrix<-function(avec,bvec){
na<-length(avec)
nb<-length(bvec)
out<-matrix(NA,nrow=na,ncol=nb)
for(i in seq_len(na)){
out[i,]<-avec[i]-bvec
}
return(out)
}
meanpercentile<-function(sample,population){
sample %>% sapply(function(x) mean(x<population,na.rm=T)) %>% mean(na.rm=T)
}
#' Compu... | /scratch/gouwar.j/cran-all/cranData/AATtools/R/aat_stimulus_rest.R |
# Score computation algorithms ####
#' @title AAT score computation algorithms
#' @name Algorithms
#' @description AAT score computation algorithms
#' @param ds A long-format data.frame
#' @param subjvar Column name of the participant identifier variable
#' @param pullvar Column name of the movement variable (0: avoi... | /scratch/gouwar.j/cran-all/cranData/AATtools/R/algorithms.R |
#' @name correlation-tools
#' @title Correlation tools
#' @description Helper functions to compute important statistics from correlation coefficients.
#' @param r,r1,r2 a correlation value
#' @param z a Z-score
#' @param n,n1,n2 sample sizes
#' @param alpha the significance level to use
#' @seealso \link{cormean}, \lin... | /scratch/gouwar.j/cran-all/cranData/AATtools/R/cortools.R |
#' AAT examining approach bias for erotic stimuli
#'
#' AAT
#'
#' @docType data
#'
#' @usage erotica
#'
#' @format An object of class \code{"data.frame"}
#'
#' @keywords datasets
#'
#' @references Kahveci, S., Van Bockstaele, B.D., & Wiers, R.W. (in preparation).
#' Pulling for Pleasure? Erotic Approach-Bias Associated... | /scratch/gouwar.j/cran-all/cranData/AATtools/R/data.R |
balancedrandombinary<-function(n){
keys<-rep(c(0,1),floor(n/2))
if(n%%2){
keys<-c(keys,NA)
}
keys[sample.int(length(keys))]
}
splitsweep<-function(currsplitset){
h<-tapply(seq_len(nrow(currsplitset)),currsplitset,function(x){
cbind(x,balancedrandombinary(length(x)))
},simplify=F)
h<-do.call(rbind... | /scratch/gouwar.j/cran-all/cranData/AATtools/R/datasplitter.R |
serr<-function(x,na.rm=T){sqrt(var(x,na.rm=na.rm)/sum(!is.na(x)))}
FlanaganRulonBilateral<-function(x1,x2){
key<-!is.na(x1) & !is.na(x2)
x1<-x1[key]
x2<-x2[key]
fr<-(1-var(x1-x2)/var(x1+x2))
return(fr/max(1, 1-fr))
}
RajuBilateral<-function(x1,x2,prop){
covar<-cov(x1,x2)
sumvar<-var(x1)+var(x2)+2*abs(co... | /scratch/gouwar.j/cran-all/cranData/AATtools/R/helpers.R |
# Outlier removing algorithms ####
#' @title Pre-processing rules
#' @description These are pre-processing rules that can be used in \link{aat_splithalf}, \link{aat_bootstrap}, and \link{aat_compute}.
#'
#' \itemize{
#' \item The following rules are to be used for the \code{trialdropfunc} argument.
#' The way you hand... | /scratch/gouwar.j/cran-all/cranData/AATtools/R/outlierhandlers.R |
#' Compute psychological experiment reliability
#' @description This function can be used to compute an exact reliability score for a psychological task whose results involve a difference score.
#' The resulting intraclass correlation coefficient is equivalent to the average all possible split-half reliability scores.... | /scratch/gouwar.j/cran-all/cranData/AATtools/R/q_reliability.R |
# utils ####
#' @name splitrel
#' @title Split Half-Based Reliability Coefficients
#' @seealso \link{covrel}
NULL
#' @describeIn splitrel Perform a Spearman-Brown correction on the provided correlation score.
#'
#' @param corr To-be-corrected correlation coefficient
#' @param ntests An integer indicating how many tim... | /scratch/gouwar.j/cran-all/cranData/AATtools/R/relcorrections.R |
#' @import dplyr
#' @import magrittr
#' @import doParallel
#' @import foreach
#' @importFrom magrittr %>% %<>% %$%
#' @importFrom dplyr group_by ungroup mutate summarise sample_n n filter select
#' @importFrom parallel detectCores makeCluster stopCluster
#' @importFrom foreach getDoParRegistered registerDoSEQ
#' @impor... | /scratch/gouwar.j/cran-all/cranData/AATtools/R/zzz.R |
#' Shiny App to Demonstrate Analysis of Variance
#'
#' @name shiny_anova
#' @aliases shiny_anova
#' @description An interactive Shiny app to demonstrate Analysis of Variance.
#' @usage shiny_anova()
#'
#' @details The interactive Shiny app demonstrates the principles of Analysis of Variance.
#' The true parameter v... | /scratch/gouwar.j/cran-all/cranData/ABACUS/R/shiny_anova.R |
#' Shiny App to Explore Properties of the Normal Distribution
#'
#' @name shiny_dnorm
#' @aliases shiny_dnorm
#' @description An interactive Shiny app to demonstrate properties of the Normal distribution.
#' @usage shiny_dnorm()
#'
#' @details The interactive Shiny app demonstrates the properties of Normal distribution... | /scratch/gouwar.j/cran-all/cranData/ABACUS/R/shiny_dnorm.R |
#' Shiny App to Explore Properties of Normal and Student's t Distributions
#'
#' @name shiny_dnorm_dt
#' @aliases shiny_dnorm_dt
#' @description An interactive Shiny app to demonstrate Normal and Student's t distributions.
#' @usage shiny_dnorm_dt()
#'
#' @details The interactive Shiny app demonstrates the properties o... | /scratch/gouwar.j/cran-all/cranData/ABACUS/R/shiny_dnorm_dt.R |
#' Shiny App to Demonstrate One-Sample Student's t-Test
#'
#' @name shiny_onesampt
#' @aliases shiny_onesampt
#' @description An interactive Shiny app to demonstrate one-sample Student's t-test.
#' @usage shiny_onesampt()
#'
#' @details The interactive Shiny app demonstrates the principles of the hypothesis testing of ... | /scratch/gouwar.j/cran-all/cranData/ABACUS/R/shiny_onesampt.R |
#' Shiny App to Demonstrate One-Sample Z-Test
#'
#' @name shiny_onesampz
#' @aliases shiny_onesampz
#' @description An interactive Shiny app to demonstrate one-sample Z-test.
#' @usage shiny_onesampz()
#'
#' @details The interactive Shiny app demonstrates the principles of the hypothesis testing of means
#' in a on... | /scratch/gouwar.j/cran-all/cranData/ABACUS/R/shiny_onesampz.R |
#' Shiny App to Explore Properties of Sampling Distributions
#'
#' @name shiny_sampling
#' @aliases shiny_sampling
#' @description An interactive Shiny app to demonstrate properties of the sampling distributions.
#' @usage shiny_sampling()
#'
#' @details The interactive Shiny app demonstrates the properties of the samp... | /scratch/gouwar.j/cran-all/cranData/ABACUS/R/shiny_sampling.R |
#' Shiny App to Demonstrate Two-Sample Independent (Unpaired) Student's t-Test
#'
#' @name shiny_twosampt
#' @aliases shiny_twosampt
#' @description An interactive Shiny app to demonstrate two-sample independent (unpaired) Student's t-test.
#' @usage shiny_twosampt()
#'
#' @details The interactive Shiny app demonstrate... | /scratch/gouwar.j/cran-all/cranData/ABACUS/R/shiny_twosampt.R |
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