ndl (version 0.2.18)

ndlCuesOutcomes: Creation of dataframe for Naive Discriminative Learning from formula specification

Description

ndlCuesOutcomes creates a dataframe for fitting a naive discriminative classification model with ndlClassify, using the specified formula and provided data.

Usage

ndlCuesOutcomes(formula, data, frequency=NA, 
  numeric2discrete=function(x) Hmisc::cut2(x,g=g.numeric), g.numeric=2,
  check.values=TRUE, ignore.absent=NULL, variable.value.separator="", …)

Arguments

formula

An object of class formula (or one that can be coerced to that class): a symbolic description of the model to be fitted.

data

A data frame containing the variables in the model.

frequency

A numeric vector (or the name of a column in the input data frame) with the frequencies of the exemplars. If absent, each exemplar is assigned a frequency equal to 1.

numeric2discrete

A function to transform a continuous numeric predictor into a number of discrete classes, by default cut2 from the Hmisc package. If set to NULL, each value of each numeric predictor will be treated as a discrete class of its own.

g.numeric

A parameter to be passed to the numeric2discrete function (parameter g for Hmisc::cut2(..., g=g.numeric, ...), or a user-defined function), determining the desired number of discrete categories for each numeric predictor; by default equal to 2.

check.values

A logical specifying whether underscores ‘_’ in predictor values should substituted with periods ‘.’; if =FALSE, the predictor values will be only checked and an error message will result if any underscores are discovered.

ignore.absent

A character vector specifying one or more values for any predictor (e.g. NIL, None and/or Other) which may be considered truely absent cues in terms of the Rescorla-Wagner equations; by default set to NULL so that all values of all predictors will be treated as present cues.

variable.value.separator

A character string which will separate variable names from variable values in their combination as cue values; by default an empty character string (="").

…

Control arguments to be passed along to estimateWeights.

Value

A dataframe with the following columns:

Frequency

Frequency with which the specific Cues and Outcomes co-occur.

Cues

A character vector of sets of Cues per instance, with Cues separated by underscore ‘_’.

Outcomes

A character vector of Outcomes per instance.

Details

Creates a dataframe to be used for fitting a Naive Discriminatory Learning classifier model.

References

Arppe, A. and Baayen, R. H. (in prep.) Statistical modeling and the principles of human learning.

See Also

cueCoding, ndlClassify

Examples

# NOT RUN {
data(think)
set.seed(314)
think <- think[sample(1:nrow(think),500),]
think.CuesOutcomes <- ndlCuesOutcomes(Lexeme ~ (Person * Number * Agent) + Register, 
data=think)
head(think.CuesOutcomes)

# }
# NOT RUN {
data(dative)
dative.cuesOutcomes <- ndlCuesOutcomes(RealizationOfRecipient ~ LengthOfRecipient +
   LengthOfTheme, data=dative, numeric2discrete=NULL)
table(dative.cuesOutcomes$Cues)

dative.cuesOutcomes1 <- ndlCuesOutcomes(RealizationOfRecipient ~ LengthOfRecipient +
   LengthOfTheme, data=dative)
table(dative.cuesOutcomes1$Cues)

dative.cuesOutcomes2 <- ndlCuesOutcomes(RealizationOfRecipient ~ LengthOfRecipient +
   LengthOfTheme, data=dative, g.numeric=3)
table(dative.cuesOutcomes2$Cues)

# }