ndlCuesOutcomes: Creation of dataframe for Naive Discriminative Learning from...
In ndl: Naive Discriminative Learning

Description Usage Arguments Details Value Author(s) References See Also Examples

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

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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="", ...)

`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`.

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

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.

R. H. Baayen and Antti Arppe

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

cueCoding, ndlClassify

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)


## End(Not run)