predict.cv.MSTweedie: Prediction based on a cv.MSTweedie object

In fontaine618/MSTweedie: Multi-source Sparse Tweedie Modelling

Description

This function is a wrapper function for the predict.MSTweedie when applied to a cv.MSTweedie object.

Usage

## S3 method for class 'cv.MSTweedie'
predict(object, newx, s = c("lambda.1se", "lambda.min"))

Arguments

object	cv.MSTweedie object.
newx	A list of matrices all with nvars columns used to predict the responses. Each matrix is associated with a source from the original object MSTweedie object in the order of argument tasks. Default is the original dataset (object$MSTweedie.fit$x).
s	Either a vector of regularization parameters (must match those of object), a vector of indices of regularization parameters. Default is the whole solution path or one of "lambda.min" or "lambda.1se". Default is "lambda.1se".

Value

A list of length ntasks of vectors corresponding to the prediction of either the reponses or the link function (see type).

Author(s)

Simon Fontaine, Yi Yang, Bo Fan, Wei Qian and Yuwen Gu.

Maintainer: Simon Fontaine [e-mail]

References

Fontaine, S., Yang, Y., Fan, B., Qian, W. and Gu, Y. (2018). "A Unified Approach to Sparse Tweedie Model with Big Data Applications to Multi-Source Insurance Claim Data Analysis," to be submitted.

See Also

cv.MSTweedie, predict.MSTweedie

Examples

# import package
library(MSTweedie)

# load data
data(AutoClaim)

# performs 10-folds CV with L1/Linf regularization
cv <- cv.MSTweedie(x = AutoClaim, y=1, source=4, reg='Linf')

# extract coefficients at lambda.1se
head(predict.cv.MSTweedie(cv)[[1]])

fontaine618/MSTweedie documentation built on May 25, 2019, 5:22 p.m.