The design matrix, without an intercept, as in biglasso.
y
The response vector, as in biglasso.
row.idx
The integer vector of row indices of X that used for fitting
the model. as in biglasso.
...
Additional arguments to biglasso.
ncores
cv.biglasso can be run in parallel across a
cluster using the parallel package. If ncores > 1, then a cluster is
created to run cv.biglasso in parallel. The code is run in series if
ncores = 1 (the default). An error occurs if ncores is larger than the
total number of available cores. Since each core takes (around equally) a large portion
of memory, the total memory consumed would be proportional to ncores.
Be cautious here to prevent the memory usage from blowing up in the big data case.
nfolds
The number of cross-validation folds. Default is 10.
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 cv.biglasso.
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.
Details
The function calls biglassonfolds times, each time
leaving out 1/nfolds of the data. The cross-validation
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.
Value
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 lambda, averaged
across the cross-validation folds.
cvse
The estimated standard error associated with each value of
for cve.
lambda
The sequence of regularization parameter values along
which the cross-validation error was calculated.
fit
The fitted biglasso object for the whole data.
min
The index of lambda corresponding to
lambda.min.
lambda.min
The value of lambda with the minimum
cross-validation error.
null.dev
The deviance for the intercept-only model.
pe
If family="binomial", the cross-validation prediction
error for each value of lambda.
## cv.biglasso
seed <- 1234
data(prostate)
X <- as.matrix(prostate[,1:8])
y <- prostate$lpsa
X <- as.big.matrix(X)
# run in series
cvfit <- cv.biglasso(X, y, family = 'gaussian', seed = seed)
par(mfrow = c(2, 2))
plot(cvfit, type = 'all')
summary(cvfit)
# run in parallel
## Not run:
cvfit2 <- cv.biglasso(X, y, family = 'gaussian', seed = seed, ncores = 5)
plot(cvfit2)
summary(cvfit2)
stopifnot(identical(cvfit, cvfit2))
## End(Not run)
providing 
.