# Create event training data from a frequency data frame. ```r createTrainingData( data, nruns = 1, random = TRUE, within.runs = FALSE, add.id = TRUE, check = TRUE ) ``` ## Arguments - `data`: Data frame with columns `Cues` and `Outcomes`, and optionally `Frequency`. - `nruns`: Numeric: number of times to run through the data. - `random`: Logical: randomize the data or not (defaults to TRUE). - `within.runs`: Logical: apply setting of `random` to the data _within_ each run (if set to TRUE) or over all data (if set to FALSE). Default setting is FALSE. Note that to randomize the data within seprate runs, both `random` and `within.runs` should be set to TRUE. - `add.id`: Logical: whether or not to add columns that identify events (default is TRUE). The column `Item` is added to describe each type of event (unless this column already exists in `data`), the column `Run` is added when `within.runs=TRUE`, and the column `Trial` indicates the order of events within the data frame or within the run (when `within.runs=TRUE`). - `check`: Logical: check for empty strings ("") or not (defaults to TRUE). If empty strings are found, they will be removed. ## Returns data frame ## Examples ```r # load example data: data(dat) # add obligatory columns Cues, Outcomes, and Frequency: dat$Cues <- paste("BG", dat$Shape, dat$Color, sep="_") dat$Outcomes <- dat$Category dat$Frequency <- dat$Frequency1 head(dat) dim(dat) # now use createTrainingData to sample from the specified frequencies: train <- createTrainingData(dat) head(train) dim(train) # the rows should be equal to the sum of frequencies in dat: sum(dat$Frequency) # this training data can actually be used train network: wm <- RWlearning(train) # inspect weight matrix: wm[[1]] # retrieve cues and outcomes from data: c <- getCues(wm) o <- getOutcomes(wm) # add missing cues to initial weight matrix: checkWM(c, o, wm=wm[[1]]) # ------------------- # additional possibility for # simulating experimental designs: # ------------------- dat$Frequency <- dat$Frequency2 train2 <- createTrainingData(dat, nruns=5) head(train2) # items are completely randomized, # and not equally distributed over the experiment: train2$Run <- rep(1:5, each=(nrow(train2)/5)) table(train2$Run, train2$Item) # in this way the items are randomized within each run: train3 <- createTrainingData(dat, nruns=5, within.runs=TRUE) head(train3) table(train3$Run, train3$Item) # difference in learning (may take some time): ## Not run: wm2 <- RWlearning(train2) plotCueWeights(wm2, cue="brown") wm3 <- RWlearning(train3) plotCueWeights(wm3, cue="brown") plotOutcomeWeights(wm3, outcome="animal") ## End(Not run) ``` ## Author(s) Jacolien van Rij

createTrainingData function