coef.cv.gglasso | R Documentation |
This function gets coefficients or makes coefficient predictions from a
cross-validated gglasso
model, using the stored "gglasso.fit"
object, and the optimal value chosen for lambda
.
## S3 method for class 'cv.gglasso'
coef(object, s = c("lambda.1se", "lambda.min"), ...)
object |
fitted |
s |
value(s) of the penalty parameter |
... |
not used. Other arguments to predict. |
This function makes it easier to use the results of cross-validation to get coefficients or make coefficient predictions.
The coefficients at the requested values for lambda
.
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
Friedman, J., Hastie, T., and Tibshirani, R. (2010), "Regularization paths
for generalized linear models via coordinate descent," Journal of
Statistical Software, 33, 1.
http://www.jstatsoft.org/v33/i01/
cv.gglasso
, and predict.cv.gglasso
methods.
# 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.1se
pre = coef(cv$gglasso.fit, s = cv$lambda.1se)
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