predict.islasso function

Prediction method for islasso fitted objects

## 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.

Returns

An object depending on the type argument

Author(s)

Maintainer: Gianluca Sottile gianluca.sottile@unipa.it

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")