edl (version 1.1)

plotCueWeights: Visualize the change of connection weights between a specific cue and all outcomes.

Description

Visualize the change of connection weights between a specific cue and all outcomes.

Usage

plotCueWeights(
  wmlist,
  cue,
  select.outcomes = NULL,
  init.value = 0,
  add.labels = TRUE,
  add = FALSE,
  ...
)

Arguments

wmlist

A list with weightmatrices, generated by RWlearning or updateWeights.

cue

String: cue for which to extract the connection weights.

select.outcomes

Optional selection of outcomes 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 getOutcomes for returning all outcomes from the data, and getValues for returning all outcomes in the data.

init.value

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

add.labels

Logical: whether or not to add labels for the lines. Defaults to TRUE, see examples.

add

Logical: whether or not to add the lines to an existing plot. Defaults to FALSE (starting a new plot).

...

Optional graphical arguments, as specified in par. These parameters are forwarded to the functions emptyPlot, lines, and text.

Value

Optionally a list with label specifications is returned, which allows to plot your own labels. This may be helpful for very long labels, and for overlapping lines.

See Also

plotOutcomeWeights, getWeightsByOutcome, 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)

# plot connection weights for cue = 'car':
plotCueWeights(wm, cue="car")

# plot your own labels:
labels <- plotCueWeights(wm, cue="car", add.labels=FALSE)
legend_margin('topright', legend=labels$labels, col=labels$col, 
    lwd=1, bty='n')

# change color and select outcomes:
out <- getValues(train$Outcomes, unique=TRUE)
out <- out[out != "vehicle"]
labels <- plotCueWeights(wm, cue="car", add.labels=FALSE, 
    col=alphaPalette(c(1,2), f.seq=rep(.5,length(out))), 
    select.outcomes=out)
lab2 <- plotCueWeights(wm, cue="car", add.labels=FALSE, 
    select.outcomes="vehicle", add=TRUE, col=1, lwd=2)
legend_margin('topright', legend=c(labels$labels, "vehicle"), 
    col=c(labels$col, lab2$col), lwd=c(labels$lwd, lab2$lwd), 
    lty=c(labels$lty, lab2$lty))

# }