ndlCrossvalidate: Crossvalidation of a Naive Discriminative Learning model.
In ndl: Naive Discriminative Learning

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

ndlCrossvalidate undertakes a crossvalidation of a Naive Discriminative Learning model fitted using ndlClassify.

1	ndlCrossvalidate(formula, data, frequency=NA, k=10, folds=NULL, ...)

`formula`	An object of class `formula` (or one that can be coerced to that class): a symbolic description of the model to be fitted. If alternatively set to `=NA`, the `data` argument is expected to be in the `ndl` internal format as generated by `ndlCuesOutcomes`, and `ndlCrossvalidate` will check that this is the case.
`data`	A data frame (as in `ndlClassify`) containing the variables in the `formula` specifying 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.
`k`	The number of folds, by default equal to 10.
`folds`	A list of user-defined folds, each item on the list representing a vector of indices indicating lines in the data frame to be used for testing a model fitted with the rest of the data. By default `NULL`, so that the folds are determined with random selection by the function `ndlCrossvalidate`.
`...`	Control arguments to be passed along to auxiliary functions, in specific `estimateWeights` and/or `estimateActivations`.

Crossvalidation of a Naive Discriminative Learning model.

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

call: The call matched by ndlCrossvalidate
formula: The formula specified for ndlCrossvalidate
fits: A list of individual fits resulting from ndlCrossvalidate
k: The number of folds, by default equal to 10
n.total: The sum frequency of data points
n.train: The size of the training set
n.test: The size of of the testing set
folds: A list with the folds used in the crossvalidation; either selected at random by ndlCrossvalidate or provided by the user.

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.

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

summary.ndlCrossvalidate, ndlStatistics, ndlCuesOutcomes, cueCoding, estimateWeights, estimateActivations

data(think)
set.seed(314)
think <- think[sample(1:nrow(think),500),]
think.cv5 <- ndlCrossvalidate(Lexeme ~ Agent + Patient, data=think, k=5)
summary(think.cv5)
rm(think)

## Not run: 
data(think)
think.cv10 <- ndlCrossvalidate(Lexeme ~ Person + Number + Agent + Patient + Register,
   data=think, k=10)
summary(think.cv10)

## End(Not run)
## Not run: 
library(languageR)
data(finalDevoicing)
finDev.cv10 <- ndlCrossvalidate(Voice ~ Onset1Type + Onset2Type + VowelType *
   ConsonantType * Obstruent + Nsyll + Stress, data=finalDevoicing, k=10)
summary(finDev.cv10)


## End(Not run)