predict.islasso.path {islasso} | R Documentation |
Prediction method for islasso.path fitted objects
Description
Prediction method for islasso fitted objects
Usage
## S3 method for class 'islasso.path'
predict(object, newdata, type = c("link", "response",
"coefficients", "class"), lambda, ...)
Arguments
object |
a fitted object of class |
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 |
lambda |
Value(s) of the penalty parameter lambda at which predictions are required. Default is the entire sequence used to create the model. |
... |
further arguments passed to or from other methods. |
Value
An object depending on the type argument
Author(s)
Maintainer: Gianluca Sottile <gianluca.sottile@unipa.it>
See Also
islasso.path
, islasso.path.fit
, coef.islasso.path
, residuals.islasso.path
, GoF.islasso.path
, logLik.islasso.path
, fitted.islasso.path
, summary.islasso.path
and deviance.islasso.path
methods.
Examples
## Not run:
set.seed(1)
n <- 100
p <- 30
p1 <- 10 #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)
o <- islasso.path(y ~ ., data = data.frame(y = y, X),
family = gaussian())
temp <- GoF.islasso.path(o)
predict(o, type = "response", lambda = temp$lambda.min)
## End(Not run)