cv.biglasso {biglasso}  R Documentation 
Perform kfold cross validation for penalized regression models over a grid of values for the regularization parameter lambda.
cv.biglasso( X, y, row.idx = 1:nrow(X), eval.metric = c("default", "MAPE"), ncores = parallel::detectCores(), ..., nfolds = 5, seed, cv.ind, trace = FALSE )
X 
The design matrix, without an intercept, as in

y 
The response vector, as in 
row.idx 
The integer vector of row indices of 
eval.metric 
The evaluation metric for the crossvalidated error and
for choosing optimal 
ncores 
The number of cores to use for parallel execution across a
cluster created by the 
... 
Additional arguments to 
nfolds 
The number of crossvalidation folds. Default is 5. 
seed 
The seed of the random number generator in order to obtain reproducible results. 
cv.ind 
Which fold each observation belongs to. By default the
observations are randomly assigned by 
trace 
If set to TRUE, cv.biglasso will inform the user of its progress by announcing the beginning of each CV fold. Default is FALSE. 
The function calls biglasso
nfolds
times, each time leaving
out 1/nfolds
of the data. The crossvalidation error is based on the
residual sum of squares when family="gaussian"
and the binomial
deviance when family="binomial"
.
The S3 class object
cv.biglasso
inherits class cv.ncvreg
. So S3
functions such as "summary", "plot"
can be directly applied to the
cv.biglasso
object.
An object with S3 class "cv.biglasso"
which inherits from
class "cv.ncvreg"
. The following variables are contained in the
class (adopted from cv.ncvreg
).
cve 
The error
for each value of 
cvse 
The estimated standard error associated with each value
of for 
lambda 
The sequence of regularization parameter values along which the crossvalidation error was calculated. 
fit 
The fitted 
min 
The index of 
lambda.min 
The value of 
null.dev 
The deviance for the interceptonly model. 
pe 
If 
cv.ind 
Same as above. 
Yaohui Zeng and Patrick Breheny
Maintainer: Yaohui Zeng <yaohui.zeng@gmail.com>
biglasso
, plot.cv.biglasso
,
summary.cv.biglasso
, setupX
## Not run: ## cv.biglasso data(colon) X < colon$X y < colon$y X.bm < as.big.matrix(X) ## logistic regression cvfit < cv.biglasso(X.bm, y, family = 'binomial', seed = 1234, ncores = 2) par(mfrow = c(2, 2)) plot(cvfit, type = 'all') summary(cvfit) ## End(Not run)