edl (version 1.1)

getWeightsByOutcome: Extract the change of connection weights between all cues and a specific outcome.

Description

Extract the change of connection weights between all cues and a specific outcome. The values are returned as data frame.

Usage

getWeightsByOutcome(wmlist, outcome, select.cues = NULL, init.value = 0)

Arguments

wmlist

A list with weightmatrices, generated by RWlearning or updateWeights.

outcome

String: outcome for which to extract the connection weights.

select.cues

Optional selection of cues to limit the number of connection weights that are returned. The value of NULL (default) will return all connection weights. Note that specified values that are not in the weightmatrices will return the initial value without error or warning. Please use getCues for returning all cues from the data, and getValues for returning all cues in the data.

init.value

Value of connection weights for non-existing connections. Typically set to 0.

Value

Data frame.

See Also

plotCueWeights, plotOutcomeWeights, getWeightsByCue

Examples

# NOT RUN {
# 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:
wm <- RWlearning(train)

# final weight matrix:
getWM(wm)

# Inspect the change in connection weights 
# for cue=car
outweights <- getWeightsByOutcome(wm, outcome='vehicle')
head(outweights)
emptyPlot(nrow(outweights), range(outweights), h0=0,
    main="Outcome='vehicle'", ylab='connection weights', xlab='learning events')
lines(outweights$BG)
lines(outweights$car, lty=4)
lines(outweights$bicycle, lty=2)
lines(outweights$cat, col=2)
lines(outweights$red, col='blue', lty=4)
lines(outweights$gray, col='blue', lty=2)
legend('bottomright', legend=c('BG', 'car', 'bicycle', 'cat', 'red', 'gray'),
    col=c(1,1,1,2,'blue', 'blue'), lty=c(1,4,2,1,4,2), lwd=1)

# }