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

In gglasso: Group Lasso Penalized Learning Using a Unified BMD Algorithm

make predictions from a "gglasso" object.

Description

Similar to other predict methods, this functions predicts fitted values and class labels from a fitted gglasso object.

Usage

## S3 method for class 'gglasso'
predict(object, newx, s = NULL, type = c("class", "link"), ...)

Arguments

object	fitted gglasso model object.
newx	matrix of new values for x at which predictions are to be made. Must be a matrix.
s	value(s) of the penalty parameter lambda at which predictions are required. Default is the entire sequence used to create the model.
type	type of prediction required: Type "link", for regression it returns the fitted response; for classification it gives the linear predictors. Type "class", only valid for classification, it produces the predicted class label corresponding to the maximum probability.
...	Not used. Other arguments to predict.

Details

s is the new vector at which predictions are requested. If s is not in the lambda sequence used for fitting the model, the predict function will use linear interpolation to make predictions. The new values are interpolated using a fraction of predicted values from both left and right lambda indices.

Value

The object returned depends on type.

Author(s)

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

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

coef method

Examples

# load gglasso library
library(gglasso)

# load data set
data(colon)

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

# fit group lasso
m1 <- gglasso(x=colon$x,y=colon$y,group=group,loss="logit")

# predicted class label at x[10,]
print(predict(m1,type="class",newx=colon$x[10,]))

# predicted linear predictors at x[1:5,]
print(predict(m1,type="link",newx=colon$x[1:5,]))

gglasso documentation built on May 29, 2024, 2:38 a.m.