predict.islasso {islasso}

Prediction method for islasso fitted objects

Description

Prediction method for islasso fitted objects

Usage

## S3 method for class 'islasso'
predict(object, newdata = NULL, 
  type = c("link", "response", "coefficients", "class", "terms"), 
  se.fit = FALSE, ci = NULL, type.ci = "wald", 
  level = .95, terms = NULL, na.action = na.pass, ...)

Arguments

object

a fitted object of class "islasso".

newdata

optionally, a data frame in which to look for variables with which to predict. If omitted, the fitted linear predictors are used.

type

the type of prediction required. The default is on the scale of the linear predictors; the alternative "response" is on the scale of the response variable. Thus for a default binomial model the default predictions are of log-odds (probabilities on logit scale) and type = "response" gives the predicted probabilities. The coefficients option returns coefficients. Type "class" applies only to "binomial" models, and produces the class label. The "terms" option returns a matrix giving the fitted values of each term in the model formula on the linear predictor scale.

se.fit

logical switch indicating if confidence intervals are required.

ci

optionally, a two columns matrix of estimated confidence intervals for the estimated coefficients.

type.ci

Only Wald-type confidence intervals are implemented yet! type.ci = "wald" estimates and standard errors are used to build confidence interval

level

the confidence level required.

terms

with type = "terms" by default all terms are returned. A character vector specifies which terms are to be returned.

na.action

function determining what should be done with missing values in newdata. The default is to predict NA.

...

further arguments passed to or from other methods.

Value

An object depending on the type argument

Author(s)

Maintainer: Gianluca Sottile <[e-mail]>

See Also

islasso.fit, summary.islasso, residuals.islasso, logLik.islasso, predict.islasso and deviance.islasso methods.

Examples

 set.seed(1)
 n <- 100
 p <- 100
 p1 <- 20  #number of nonzero coefficients
 coef.veri <- sort(round(c(seq(.5, 3, l=p1/2), seq(-1, -2, l=p1/2)), 2))
 sigma <- 1

 coef <- c(coef.veri, rep(0, p-p1))

 X <- matrix(rnorm(n*p), n, p)
 mu <- drop(X%*%coef)
 y <- mu + rnorm(n, 0,sigma)
 lambda <- 2
 o <- islasso(y ~ ., data = data.frame(y = y, X), lambda = lambda)
 predict(o, type = "response")