train {emuR}

Train a Gaussian Model

Description

Trains a Gaussian Model

Usage

train(x, lab = rep("x", nrow(x)))

Arguments

x

A data vector or matrix.

lab

A vector of labels parallel to x. If missing, all data is assumed to be from the same class.

Details

This function is used to train a gaussian model on a data set. The result can be passed to either the mahal or bayes.lab functions to classify either the training set (x) or a test set with the same number of dimensions. Train simply finds the mean and inverse covariance matrix/standard deviation for the data corresponding to each unique label in labs.

Value

A structure with the following components:

label

The unique labels in lab.

means

The means for each dimension per unique label.

cov

The combined covariance matrixes for each unique label. The matrixes are joined with rbind. If the input data is one-dimensional, this is just the standard deviation of the data.

invcov

The combined inverse covariance matrixes for each unique label. The matrixes are joined with rbind. If the input data is one-dimensional, this is just the reciprocal of the standard deviation of the data.

See Also

mahal, bayes.lab, mahalplot, bayes.plot