emuR: train – R documentation

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emuR

train

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.