predict.nmfkc: Prediction method for objects of class 'nmfkc'

In nmfkc: Non-Negative Matrix Factorization with Kernel Covariates

Prediction method for objects of class nmfkc

Description

predict.nmfkc generates predictions from an object of class nmfkc, either using the fitted covariates or a new covariate matrix.

When the model was fitted using a formula (Formula Mode), a newdata data frame can be supplied instead of newA; the covariate matrix is then constructed automatically from the stored formula metadata.

Usage

## S3 method for class 'nmfkc'
predict(object, newA = NULL, newdata = NULL, type = "response", ...)

Arguments

object	An object of class nmfkc, i.e., the return value of nmfkc.
newA	Optional. A new covariate matrix to be used for prediction.
newdata	Optional data frame. Only available when the model was fitted using a formula. Covariate columns are extracted automatically using the stored formula metadata. If both newdata and newA are supplied, newdata takes precedence (with a warning).
type	Type of prediction to return. Options are "response" (fitted values matrix), "prob" (soft-clustering probabilities), or "class" (hard-clustering labels based on row names of X).
...	Further arguments passed to or from other methods.

Value

Depending on type: a numeric matrix ("response" or "prob") or a character vector of class labels ("class").

See Also

nmfkc, nmfkc.cv

Examples

# Prediction with newA
Y <- matrix(cars$dist, nrow = 1)
A <- rbind(1, cars$speed)
result <- nmfkc(Y, A, rank = 1)
newA <- rbind(1, c(10, 20, 30))
predict(result, newA = newA)

nmfkc documentation built on July 14, 2026, 1:07 a.m.