stepPlr (version 0.93)

# predict.plr: prediction function for plr

## Description

This function computes the linear predictors, probability estimates, or the class labels for new data, using a `plr` object.

## Usage

```# S3 method for plr
predict(object, newx = NULL,
type = c("link", "response", "class"), ...)```

## Arguments

object

`plr` object

newx

matrix of features at which the predictions are made. If `newx=NULL,` predictions for the training data are returned.

type

If `type=link,` the linear predictors are returned; if `type=response,` the probability estimates are returned; and if `type=class,` the class labels are returned. Default is `type=link.`

...

other options for prediction

## References

Mee Young Park and Trevor Hastie (2008) Penalized Logistic Regression for Detecting Gene Interactions

plr

## Examples

```# NOT RUN {
n <- 100

p <- 10
x0 <- matrix(rnorm(n * p), nrow=n)
y <- sample(c(0, 1), n, replace=TRUE)
fit <- plr(x0, y, lambda=1)
x1 <- matrix(rnorm(n * p), nrow=n)
pred2 <- predict(fit, x1, type="response")
pred3 <- predict(fit, x1, type="class")

p <- 3
z <- matrix(sample(seq(3), n * p, replace=TRUE), nrow=n)
x0 <- data.frame(x1=factor(z[, 1]), x2=factor(z[, 2]), x3=factor(z[, 3]))
y <- sample(c(0, 1), n, replace=TRUE)
fit <- plr(x0, y, lambda=1)
z <- matrix(sample(seq(3), n * p, replace=TRUE), nrow=n)
x1 <- data.frame(x1=factor(z[, 1]), x2=factor(z[, 2]), x3=factor(z[, 3]))