cl_max_correlation: A maximum correlation coefficient classifier (CL)

In emeyers/NDTr: The Neural Decoding Toolbox in R

Description

An implementation of a maximum correlation coefficient classifier.

Usage

cl_max_correlation()

Details

This CL object learns a mean population vector (template) for each class from the training set (by averaging together the all training points within each class). The classifier is tested by calculated Pearson’s correlation coefficient between a test point and the templates learned from the training set, and the class with the highest correlation value is returned as the predicted label. The decision values returned by the classifier are the correlation coefficients between all test points and all templates.

Like all classifiers (CL) objects, this classifier has a get_predictions() method which learns a model based on training data and then makes predictions on the test data.

See Also

Other classifier: cl_poisson_naive_bayes(), cl_svm()

Examples

# running a basic decoding analysis using the cl_max_correlation
data_file <- system.file(file.path("extdata", "ZD_150bins_50sampled.Rda"),
                         package = "NDTr")
ds <- ds_basic(data_file, "stimulus_ID", 18)
fps <- list(fp_zscore())

cl <- cl_max_correlation()
cv <- cv_standard(ds, cl, fps)
## Not run: 
DECODING_RESULTS <- run_decoding(cv)

## End(Not run)

emeyers/NDTr documentation built on Aug. 8, 2020, 3:41 p.m.