edl (version 1.1)

activationsMatrix: Calculate the activations for one or a set of cues.

Description

Calculate the activations for one or a set of cues. The values are returned as vector or data frame.

Usage

activationsMatrix(
  wm,
  cues,
  split = "_",
  select.outcomes = NULL,
  init.value = 0,
  normalize = FALSE
)

Arguments

wm

A weightmatrix, generated by RWlearning or updateWeights.

cues

String or vector of strings. Each string represents a set of cues, separated by split, for which the activations will be calculated. Note: the activations will be calculated for all provided cues together, assuming these occurred in one learning event.

split

String, separator between cues.

select.outcomes

Optional selection of outcomes to limit the number of activations that are returned. The value of NULL (default) will return all activations (for each outcome in wm). Note that specified values that are not in the weightmatrix will return the initial value without error or warning. Please use getValues for returning all outcomes in the data.

init.value

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

normalize

Logical: whether or not the activation is normalized by dividing the total activation by the number of cues. Default is FALSE. If set to TRUE, the activation reflects the average activation per cue.

Value

Vector or data frame.

See Also

getWeightsByCue, getWeightsByOutcome

Other functions for calculating activations: activationsCueSet(), activationsEvents(), activationsOutcomes(), getActivations()

Examples

# NOT RUN {
# load example data:
data(dat)

# setup data:
newdat <- data.frame(Cues =paste("BG", dat$Shape, dat$Color, sep="_"),
   Outcomes  = dat$Category,
   Frequency = dat$Frequency2)
train  <- createTrainingData(newdat)
# learning:
wm     <- RWlearning(train)

# calculate activations for all outcomes:
mat    <- getWM(wm)
activationsMatrix(mat, cues="BG_tree_green")
# only accepts one set of cues - in this case all cues 
# are combined:
activationsMatrix(mat, cues=c("BG_tree", "BG_tree_brown"))
# ... which is the same as this:
activationsMatrix(mat, cues=c("BG", "BG", "tree", "tree", "brown"))
# now select one outcome:
activationsMatrix(mat, cues=c("BG", "tree"), select.outcomes="vehicle")
# cues/outcomes not in matrix:
activationsMatrix(mat, cues=c("na"), select.outcomes="new")

# }