cv.gglasso | R Documentation |
Does k-fold cross-validation for gglasso, produces a plot, and returns a
value for lambda
. This function is modified based on the cv
function from the glmnet
package.
cv.gglasso(
x,
y,
group,
lambda = NULL,
pred.loss = c("misclass", "loss", "L1", "L2"),
nfolds = 5,
foldid,
delta,
...
)
x |
matrix of predictors, of dimension |
y |
response variable. This argument should be quantitative for regression (least squares), and a two-level factor for classification (logistic model, huberized SVM, squared SVM). |
group |
a vector of consecutive integers describing the grouping of the coefficients (see example below). |
lambda |
optional user-supplied lambda sequence; default is
|
pred.loss |
loss to use for cross-validation error. Valid options are:
Default is |
nfolds |
number of folds - default is 5. Although |
foldid |
an optional vector of values between 1 and |
delta |
parameter |
... |
other arguments that can be passed to gglasso. |
The function runs gglasso
nfolds
+1 times; the first to
get the lambda
sequence, and then the remainder to compute the fit
with each of the folds omitted. The average error and standard deviation
over the folds are computed.
an object of class cv.gglasso
is returned, which is a
list with the ingredients of the cross-validation fit.
lambda |
the
values of |
cvm |
the mean
cross-validated error - a vector of length |
cvsd |
estimate of standard error of |
cvupper |
upper
curve = |
cvlower |
lower curve = |
name |
a text string indicating type of measure (for plotting purposes). |
gglasso.fit |
a fitted |
lambda.min |
The optimal value of |
lambda.1se |
The largest
value of |
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
gglasso
, plot.cv.gglasso
,
predict.cv.gglasso
, and coef.cv.gglasso
methods.
# load gglasso library
library(gglasso)
# load data set
data(bardet)
# define group index
group <- rep(1:20,each=5)
# 5-fold cross validation using group lasso
# penalized logisitic regression
cv <- cv.gglasso(x=bardet$x, y=bardet$y, group=group, loss="ls",
pred.loss="L2", lambda.factor=0.05, nfolds=5)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.