# Prediction method for islasso fitted objects ```r ## 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 ## See Also `islasso.fit`, `summary.islasso`, `residuals.islasso`, `logLik.islasso`, `predict.islasso` and `deviance.islasso` methods. ## Examples ```r 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") ```

predict.islasso function