predict.LDCA: predict function for LDCA

Description Usage Arguments Author(s) Examples

View source: R/LDCA.R

Description

predict function for LDCA

Usage

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## S3 method for class 'LDCA'
predict(object, newx, s = NULL, type = c("link", "response", "coefficients", "nonzero", "class"), exact = FALSE, offset, ...)

Arguments

object

an LDCA object

newx

new data matrix

s

lambda value at which the prediction is returned.

type

Type of prediction required. Type "link" gives the linear predictors for "binomial", "multinomial", "poisson" or "cox" models; for "gaussian" models it gives the fitted values. Type "response" gives the fitted probabilities for "binomial" or "multinomial", fitted mean for "poisson" and the fitted relative-risk for "cox"; for "gaussian" type "response" is equivalent to type "link". Type "coefficients" computes the coefficients at the requested values for s. Note that for "binomial" models, results are returned only for the class corresponding to the second level of the factor response. Type "class" applies only to "binomial" or "multinomial" models, and produces the class label corresponding to the maximum probability. Type "nonzero" returns a list of the indices of the nonzero coefficients for each value of s.

exact

By default (exact=FALSE) the predict function uses linear interpolation to make predictions for values of s that do not coincide with those used in the fitting algorithm. Currently exact=TRUE is not implemented, but prints an error message telling the user how to achieve the exact predictions. This is done my rerunning the algorithm with the desired values interspersed (in order) with the values used in the original fit. This is easily achieved via the R command lamba=sort(c(object$lambda, new.lambda))

offset

If an offset is used in the fit, then one must be supplied for making predictions (except for type="coefficients" or type="nonzero")

...

other arguments.

Author(s)

Xiaolin Yang, Han Liu

Examples

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library(glmnet)
x=matrix(rnorm(50*20),50,20)
y=rbinom(50,1,0.5)
cvfit=cv.LDCA(x,y,nfolds=5)
predict(cvfit,x[1:10,],s="lambda.min")

BigTSP documentation built on May 29, 2017, 7:14 p.m.