# predict.matrixqda: Classify Matrix Variate Observations by Quadratic... In MixMatrix: Classification with Matrix Variate Normal and t Distributions

## Description

Classify matrix variate observations in conjunction with `matrixqda`.

## Usage

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

## Arguments

 `object` object of class `matrixqda` `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 `matrixqda` object. `prior` The prior probabilities of the classes, by default the proportions in the training set or what was set in the call to `matrixqda`. `...` arguments based from or to other methods

## Details

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

## Value

Returns a list containing the following components:

`class`

The MAP classification (a factor)

`posterior`

posterior probabilities for the classes

## See Also

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

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10``` ```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 <- matrixqda(C, groups, prior) # fit model predict(D)\$posterior[1:10, ] # predict, show results of first 10 ## S3 method for class "matrixqda" ```

MixMatrix documentation built on Nov. 16, 2021, 9:25 a.m.