predict.glmpca: Predict Method for GLM-PCA Fits

In glmpca: Dimension Reduction of Non-Normally Distributed Data

Description

Predict the mean matrix from a fitted generalized principal component analysis model object.

Usage

## S3 method for class 'glmpca'
predict(object, ...)

Arguments

object	a fitted object of class inheriting from glmpca.
...	additional named arguments. Currently ignored.

Details

Let Y be the data matrix originally used to estimate the parameters in fit. The GLM-PCA model regards each element of Y as a random sample from an exponential family distribution such as a Poisson, negative binomial, or binomial likelihood. The components of a GLM-PCA fit are combined to produce the predicted mean of this distribution at each entry of Y. This matrix may be regarded as a 'denoised' version of the original data.

Value

a dense matrix of predicted mean values.

Warning

The predicted mean matrix returned by this function will have the same dimensions as the original data matrix and it will be dense even if the original data were sparse. This can exhaust available memory for large datasets, so use with caution.

See Also

glmpca, predict.glm with type='response'

glmpca documentation built on July 19, 2020, 1:06 a.m.