Trains a Gaussian Model
train(x, lab = rep("x", nrow(x)))A structure with the following components:
The unique labels in lab.
The means for each dimension per unique label.
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.
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.
A data vector or matrix.
A vector of labels parallel to x. If missing, all data is
assumed to be from the same class.
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.
mahal, bayes.lab, mahalplot, bayes.plot