RWlearningMatrix {edl} | R Documentation |
Function implementing the Rescorla-Wagner learning.
RWlearningMatrix( data, wm = NULL, alpha = 0.1, lambda = 1, beta1 = 0.1, beta2 = 0.1, progress = TRUE, ... )
data |
A data frame with columns |
wm |
A weightmatrix of class matrix, or a list with weight matrices. If not provided a new weightmatrix is returned. Note that the cues and outcomes do not necessarily need to be available as cues and outcomes in the weightmatrix: if not present, they will be added. |
alpha |
Learning parameter (scaling both positive and negative evidence adjustments), typically set to 0.1. |
lambda |
Constant constraining the connection strength. |
beta1 |
Learning parameter for positive evidence, typically set to 0.1. |
beta2 |
Learning parameter for negative evidence, typically set to 0.1. |
progress |
Logical: whether or not showing a progress bar (may slow down the process). |
... |
Parameters for the function |
A weightmatrix.
Jacolien van Rij
# 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) # this training data can actually be used train network: wm1 <- RWlearningMatrix(train) # comparison with a list: wm2 <- RWlearning(train) length(wm2) getWM(wm2) # in R markdown or knitr reports the progress bar should be turned off: wm <- RWlearningMatrix(train, progress=FALSE) # Learning in steps is also possible: wm <- RWlearningMatrix(train[1:20,]) train[21,c("Cues", "Outcomes")] wm <- RWlearningMatrix(train[21,], wm=wm)