predict.matrixlda {MixMatrix} | R Documentation |
Classify Matrix Variate Observations by Linear Discrimination
Description
Classify matrix variate observations in conjunction with matrixlda
.
Usage
## S3 method for class 'matrixlda'
predict(object, newdata, prior = object$prior, ...)
Arguments
object |
object of class |
newdata |
array or list of new observations to be classified.
If newdata is missing, an attempt will be made to retrieve the
data used to fit the |
prior |
The prior probabilities of the classes, by default the
proportions in the training set or what was set in the call to
|
... |
arguments based from or to other methods |
Details
This function is a method for the generic function predict()
for
class "matrixlda
". It can be invoked by calling predict(x)
for
an object x
of the appropriate class.
Value
Returns a list containing the following components:
class
The MAP classification (a factor)
posterior
posterior probabilities for the classes
See Also
matrixlda()
, matrixqda()
,
and matrixmixture()
Examples
set.seed(20180221)
# construct two populations of 3x4 random matrices with different means
A <- rmatrixnorm(30, mean = matrix(0, nrow = 3, ncol = 4))
B <- rmatrixnorm(30, mean = matrix(1, nrow = 3, ncol = 4))
C <- array(c(A, B), dim = c(3, 4, 60)) # combine together
groups <- c(rep(1, 30), rep(2, 30)) # define groups
prior <- c(.5, .5) # set prior
D <- matrixlda(C, groups, prior)
predict(D)$posterior[1:10, ]
## S3 method for class 'matrixlda'