predict.gglasso: make predictions from a "gglasso" object.
In emeryyi/gglasso: Group Lasso Penalized Learning Using a Unified BMD Algorithm

Description Usage Arguments Details Value Author(s) References See Also Examples

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

1 2	## S3 method for class 'gglasso' predict(object, newx, s = NULL, type = c("class", "link"), ...)

`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.

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.

The object returned depends on type.

Yi Yang and Hui Zou
Maintainer: Yi Yang <yi.yang6@mcgill.ca>

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

coef method

# 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,]))