stepPlr (version 0.92)

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"), ...)

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

See Also

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)
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")
# }

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