grpss (version 3.0.1)

predict.cv.grpreg: Model predictions for a fitted cv.grpreg object

Description

Similar to usual predict methods and predict.cv.grpreg in grpreg package.

Usage

"predict"(object, newdata, type = c("response", "class", "probability"), ...)

Arguments

object

A fitted "grpreg" object from grpss, or cv.grpreg function.

newdata

Optionally, a matrix or data frame where to predict. If omits, the fitted predictors are used.

type

The type of prediction: "response" gives the fitted values; "class" returns the predicted class for the binomial outcome; "probability" returns the predicted probabilities for the logistic regression.

...

Not used.

Value

The predicted values depending on the type.

Details

This function gives the predictions at newdata or all predictors if the argument newdata is not supplied. Typically, type = "response" is used for linear or poisson regression, and type = "class" or type = "probability" is used for logistic regression.

See Also

grpss

Examples

library(MASS)
set.seed(23)
n <- 30 # sample size
p <- 3  # number of predictors in each group
J <- 50  # group size
group <- rep(1:J,each = 3)  # group indices
X <- mvrnorm(n,seq(0,5,length.out = p*J),diag(p*J))
beta <- runif(12,-2,5)
mu <- X%*%matrix(c(beta,rep(0,p*J-12)),ncol = 1)

# linear regression with family = "gaussian"
y <-  mu + rnorm(n)

## with cross-validation
gss11 <- grpss(X,y,group,select = TRUE,cross.validation = TRUE)
predict(gss11)  # fitted values
predict(gss11, newdata = X[1,])  # predicted values at given 'newdata'

# logistic regression with family = "binomial"
set.seed(23)
y1 <- rbinom(n,1,1/(1 + exp(-mu)))
gss21 <- grpss(X,y1,group, criterion = "gDC",select = TRUE,
               family = "binomial")
predict(gss21)