cv.grpregOverlap: Cross-validation for choosing regularization parameter lambda

In grpregOverlap: Penalized Regression Models with Overlapping Grouped Covariates

Description

Performs k-fold cross validation for penalized regression models with overlapping grouped covariates over a grid of values for the regularization parameter lambda.

Usage

cv.grpregOverlap(X, y, group, ..., nfolds = 10, seed, trace = FALSE)

Arguments

X	The design matrix, without an intercept, as in grpregOverlap.
y	The response vector (or matrix), as in grpregOverlap.
group	A list of vectors containing group information, as in grpregOverlap.
...	Additional arguments to grpregOverlap.
nfolds	The number of cross-validation folds. Default is 10.
seed	Set the seed of the random number generator to obtain reproducible results.
trace	If set to TRUE, print out the progress of the cross-validation. Default is FALSE.

Details

This function is built upon cv.grpreg. The class can directly call plot function implemented for class cv.grpreg.

Value

An object with S3 class "cv.grpregOverlap", which inherits from class "cv.grpreg". The following variables are contained in the class (adopted from cv.grpreg).

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 grpreg 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.

Author(s)

Yaohui Zeng and Patrick Breheny

Maintainer: Yaohui Zeng <[e-mail]>

References

• Breheny P (2014). R package 'grpreg'. https://CRAN.R-project.org/package=grpreg/grpreg.pdf

See Also

grpregOverlap, predict.grpregOverlap, summary, and cv.grpreg.

Examples

## linear regression, a simulation demo.
set.seed(123)
group <- list(gr1 = c(1, 2, 3),
              gr2 = c(1, 4),
              gr3 = c(2, 4, 5),
              gr4 = c(3, 5),
              gr5 = c(6))
beta.latent.T <- c(5, 5, 5, 0, 0, 0, 0, 0, 5, 5, 0) # true latent coefficients.
# beta.T <- c(5, 5, 10, 0, 5, 0), true variables: 1, 2, 3, 5; true groups: 1, 4.
X <- matrix(rnorm(n = 6*100), ncol = 6)
X.latent <- expandX(X, group)
y <- X.latent %*% beta.latent.T + rnorm(100)

cvfit <- cv.grpregOverlap(X, y, group, penalty = 'grMCP')
summary(cvfit)
plot(cvfit)
par(mfrow=c(2,2))
plot(cvfit, type="all")

Example output

Loading required package: Matrix
Loading required package: grpreg
---------------------------------------------------------------
Note: Overlapping-group selection via penalized regression: 
      'p.latent' is the number of latent variables!
---------------------------------------------------------------
grMCP-penalized linear regression with n=100, p=6 (p.latent=11)
At minimum cross-validation error (lambda=0.3766):
---------------------------------------------------------------
  Nonzero        coefficients: 4
  Nonzero latent coefficients: 5
  Nonzero groups: 2
  Cross-validation error of 1.19
  Maximum R-squared: 0.99
  Maximum signal-to-noise ratio: 109.72
  Scale estimate (sigma) at lambda.min: 1.089

grpregOverlap documentation built on May 2, 2019, 4:47 a.m.