predict.matrixlda: Classify Matrix Variate Observations by Linear Discrimination
In MixMatrix: Classification with Matrix Variate Normal and t Distributions

Description Usage Arguments Details Value See Also Examples

Classify matrix variate observations in conjunction with matrixlda.

1 2	## S3 method for class 'matrixlda' predict(object, newdata, prior = object$prior, ...)

`object`	object of class `matrixlda`
`newdata`	array or list of new observations to be classified. If newdata is missing, an attempt will be made to retrieve the data used to fit the `matrixlda` object.
`prior`	The prior probabilities of the classes, by default the proportions in the training set or what was set in the call to `matrixlda`.
`...`	arguments based from or to other methods

This function is a method for the generic function predict() for class "matrixlda". It can be invoked by calling predict(x) for an object x of the appropriate class.

Returns a list containing the following components:

class: The MAP classification (a factor)
posterior: posterior probabilities for the classes

matrixlda(), matrixqda(), and matrixmixture()

set.seed(20180221)
# construct two populations of 3x4 random matrices with different means
A <- rmatrixnorm(30, mean = matrix(0, nrow = 3, ncol = 4))
B <- rmatrixnorm(30, mean = matrix(1, nrow = 3, ncol = 4))
C <- array(c(A, B), dim = c(3, 4, 60)) # combine together
groups <- c(rep(1, 30), rep(2, 30)) # define groups
prior <- c(.5, .5) # set prior
D <- matrixlda(C, groups, prior)
predict(D)$posterior[1:10, ]

## S3 method for class 'matrixlda'