stepPlr (version 0.91)

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

predict.plr(object, newx = NULL,
              type = c("link", "response", "class"), ...)

References

Mee Young Park and Trevor Hastie (2006) Penalized Logistic Regression for Detecting Gene Interactions - available at the authors' websites, http://stat.stanford.edu/~mypark or http://stat.stanford.edu/~hastie/pub.htm.

See Also

plr

Examples

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)
pred1 <- predict(fit,x1,type="link")
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]))
pred1 <- predict(fit,x1,type="link")
pred2 <- predict(fit,x1,type="response")
pred3 <- predict(fit,x1,type="class")