# Multi-mask/weight based aggregators Methods to aggregate the same vector with different masks or frequency weights. Useful for fast bootstrapping or split-half scoring. A single aggregate value of `x` is computed for each column of the mask or weight matrix. ```r mediansByMask(x, mask) meansByMask(x, mask) sdsByMask(x, mask) mediansByWeight(x, weights) meansByWeight(x, weights) sdsByWeight(x, weights) ``` ## Arguments - `x`: A vector to aggregate over with different masks or weights. - `mask`: Logical matrix where each column represents a separate vector of masks to aggregate `x` with. Only values marked `TRUE` are included in the aggregation. - `weights`: Integer matrix where each column represents frequency weights to weight the aggregation by. ## Returns a vector with each value representing an aggregate of the same single input vector but with different masks or frequency weights applied. ## Examples ```r # Demonstration of mediansByMask() x<-1:6 mask<-rbind(c(TRUE,FALSE,FALSE), c(TRUE,FALSE,FALSE), c(FALSE,TRUE,FALSE), c(FALSE,TRUE,FALSE), c(FALSE,FALSE,TRUE), c(FALSE,FALSE,TRUE)) mediansByMask(x,mask) # Compute split-halves for a single # participant, stratified by stimulus data(foodAAT) currdata<-foodAAT[foodAAT$subjectid==3,] currdata$stratfactor<- interaction(currdata$is_pull, currdata$is_target, currdata$stimid) currdata<-currdata[order(currdata$stratfactor),] groupsizes<- rle(as.character(currdata$stratfactor))$lengths mysplits<- stratifiedItersplits(splits=1000, groupsizes=groupsizes) # Median for half 1 mediansByMask(currdata$RT,mysplits==1) #How to use meansByMask() meansByMask(x,mask) sd(meansByMask(currdata$RT,mysplits==1)) # How to use sdsByMask() to compute # mask-based D-scores meansByMask(currdata$RT,mysplits==1) / sdsByMask(currdata$RT,mysplits==1) # Compute the bootstrapped # standard error of a median weights<- bootstrapWeights(size=nrow(currdata), times=1000) bootmeds<-mediansByWeight(currdata$RT,weights) sd(bootmeds) # bootstrapped standard error # Compute the bootstrapped # standard error of a mean bootmeans<-meansByWeight(currdata$RT,weights) sd(bootmeans) # bootstrapped standard error # exact standard error for comparison sd(currdata$RT)/sqrt(length(currdata$RT)) # Use sdsByWeight to compute bootstrapped D-scores bootsds<-sdsByWeight(currdata$RT,weights) # bootstrapped standard error of D-score sd(bootmeans/bootsds) ``` ## See Also colMedians , colAggregators , generateSplits ## Author(s) Sercan Kahveci

maskAggregators function