predict.plr {stepPlr} | R Documentation |
This function computes the linear predictors, probability estimates,
or the class labels for new data, using a plr
object.
predict.plr(object, newx = NULL, type = c("link", "response", "class"), ...)
object |
a plr object
|
newx |
a 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 the prediction |
Mee Young Park and Trevor Hastie
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.
plr
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")