train {emu} | R Documentation |
Trains a Gaussian Model
train(x, lab)
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.
|
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.
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.
|
mahal, bayes.lab, mahalplot, bayes.plot