gglasso (version 1.5.1)

predict.cv.gglasso: make predictions from a "cv.gglasso" object.

Description

This function makes predictions from a cross-validated gglasso model, using the stored "gglasso.fit" object, and the optimal value chosen for lambda.

Usage

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

Value

The returned object depends on the ... argument which is passed on to the predict method for gglasso objects.

Arguments

object

fitted cv.gglasso object.

newx

matrix of new values for x at which predictions are to be made. Must be a matrix. See documentation for predict.gglasso.

s

value(s) of the penalty parameter lambda at which predictions are required. Default is the value s="lambda.1se" stored on the CV object. Alternatively s="lambda.min" can be used. If s is numeric, it is taken as the value(s) of lambda to be used.

...

not used. Other arguments to predict.

Author

Yi Yang and Hui Zou
Maintainer: Yi Yang <[e-mail]>

Details

This function makes it easier to use the results of cross-validation to make a prediction.

References

Yang, Y. and Zou, H. (2015), ``A Fast Unified Algorithm for Computing Group-Lasso Penalized Learning Problems,'' Statistics and Computing. 25(6), 1129-1141.
BugReport: https://github.com/emeryyi/gglasso

See Also

cv.gglasso, and coef.cv.gglasso methods.

Examples

# load gglasso library
library(gglasso)

# load data set
data(colon)

# define group index
group <- rep(1:20,each=5)

# 5-fold cross validation using group lasso 
# penalized logisitic regression
cv <- cv.gglasso(x=colon$x, y=colon$y, group=group, loss="logit",
pred.loss="misclass", lambda.factor=0.05, nfolds=5)

# the coefficients at lambda = lambda.min, newx = x[1,]
pre = predict(cv$gglasso.fit, newx = colon$x[1:10,], 
s = cv$lambda.min, type = "class")