ndlClassify: Classification using naive discriminative learning.
In ndl: Naive Discriminative Learning

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

ndlClassify uses the equilibrium equations of Danks (2003) for the Rescorla-Wagner model (1972) to estimate association strengths (weights) for cues (typically levels of factorial predictors) to outcomes (typically a binary or polytomous response variable). Given the association strengths, the probability of a response level is obtained by summation over the weights on active incoming links.

ndlClassify(formula, data, frequency=NA, variable.value.separator="", ...)

## S3 method for class 'ndlClassify'
print(x, max.print=10, ...)

`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.
`x`	An object of the class `"ndlClassify"` fitted with `ndlClassify` to be printed with `print.ndlClassify`.
`max.print`	The maximum number of rows of the `weightMatrix` to be output when printing with `print.ndlClassify`; by default equal to 10; if set to `NA` all rows will be output.
`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 `ndlCuesOutcomes`, `estimateWeights`, `estimateActivations`, and/or `print.ndlClassify`.

Classification by naive discriminative learning.

A list of the class "ndlClassify" with the following components:

activationMatrix: A matrix specifying for each row of the input data frame the activations (probabilities) of the levels of the response variable (nrow observations by nlevels of response variable).
weightMatrix: A matrix specifying for each cue (predictor value) the association strength (weight) to each outcome (level of the response variable) (number of distinct predictor values by number of response levels).
cuesOutcomes: The input data structure for naive discriminative learning created by ndlCuesOutcomes based on the data argument (number of observations by 3: Frequency, Cues, Outcomes).
call: The call matched to fit the resulting "ndlClassify" object.
formula: The formula specified for fitting the resulting "ndlClassify" object.
data: The supplied data argument, excluding all elements not specified for the modeling task in formula and frequency.

R. H. Baayen and Antti Arppe

Baayen, R. H. and Milin, P. and Filipovic Durdevic, D. and Hendrix, P. and Marelli, M., An amorphous model for morphological processing in visual comprehension based on naive discriminative learning. Psychological Review, 118, 438-482.

Danks, D. (2003). Equilibria of the Rescorla-Wagner model. Journal of Mathematical Psychology, 47 (2), 109-121.

Rescorla, R. A., & Wagner, A. R. (1972). A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement. In Black, A. H., & Prokasy, W. F. (Eds.), Classical conditioning II: Current research and theory (pp. 64-99). New York: Appleton-Century-Crofts.

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

summary.ndlClassify, plot.ndlClassify, anova.ndlClassify, predict.ndlClassify, ndlCuesOutcomes, estimateWeights, cueCoding

data(think)
set.seed(314)
think <- think[sample(1:nrow(think),500),]
think.ndl <- ndlClassify(Lexeme ~ (Person * Number * Agent) + Register,
   data=think)
summary(think.ndl)

## Not run: 
think.ndl.SA <- ndlClassify(Lexeme ~ (Polarity + Voice + Mood + Person +
  Number + Covert + ClauseEquivalent + Agent + Patient + Manner + Time +
  Modality1 + Modality2 + Source + Goal + Quantity + Location +
  Duration + Frequency + MetaComment + ReasonPurpose + Condition +
  CoordinatedVerb)^2 + Author + Section, data=think)
summary(think.ndl.SA)

## End(Not run)

## Not run: 
data(dative)
out <- which(is.element(colnames(dative), c("Speaker","Verb")))
dative <- dative[-out]
dative.ndl <- ndlClassify(RealizationOfRecipient ~ ., data=dative)
summary(dative.ndl)


## End(Not run)