predict.gglasso {gglasso} | R Documentation |
Similar to other predict methods, this functions predicts fitted values and class labels from a fitted gglasso
object.
## S3 method for class 'gglasso' predict(object, newx, s = NULL, type=c("class","link"), ...)
object |
fitted |
newx |
matrix of new values for |
s |
value(s) of the penalty parameter |
type |
type of prediction required:
|
... |
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,]))