`cv.glmtlp.Rd`

Performs k-fold cross-validation for l0, l1, or TLP-penalized regression models
over a grid of values for the regularization parameter `lambda`

(if `penalty="l0"`

) or `kappa`

(if `penalty="l0"`

).

cv.glmtlp(X, y, ..., seed = NULL, nfolds = 10, obs.fold = NULL, ncores = 1)

X | input matrix, of dimension |
---|---|

y | response, of length nobs, as in |

... | Other arguments that can be passed to |

seed | the seed for reproduction purposes |

nfolds | number of folds; default is 10. The smallest value allowable
is |

obs.fold | an optional vector of values between 1 and |

ncores | number of cores utilized; default is 1. If greater than 1,
then |

an object of class `"cv.glmtlp"`

is returned, which is a list
with the ingredients of the cross-validation fit.

the function call

The mean cross-validated error - a vector of length
`length(kappa)`

if `penalty = "l0"`

and `length{lambda}`

otherwise.

estimate of standard error of `cv.mean`

.

a fitted glmtlp object for the full data.

the index of the `lambda`

or `kappa`

sequence that
corresponding to the smallest cv mean error.

the values of `kappa`

used in the fits, available when
`penalty = 'l0'`

.

the value of `kappa`

that gives the minimum
`cv.mean`

, available when `penalty = 'l0'`

.

the values of `lambda`

used in the fits.

value of `lambda`

that gives minimum `cv.mean`

,
available when penalty is 'l1' or 'tlp'.

null deviance of the model.

the fold id for each observation used in the CV.

The function calls `glmtlp`

`nfolds`

+1 times; the first call to get the
`lambda`

or `kappa`

sequence, and then the rest to compute
the fit with each of the folds omitted. The cross-validation error is based
on deviance (check here for more details). The error is accumulated over the
folds, and the average error and standard deviation is computed.

When `family = "binomial"`

, the fold assignment (if not provided by
the user) is generated in a stratified manner, where the ratio of 0/1 outcomes
are the same for each fold.

Shen, X., Pan, W., & Zhu, Y. (2012).
*Likelihood-based selection and sharp parameter estimation.
Journal of the American Statistical Association, 107(497), 223-232.*

Shen, X., Pan, W., Zhu, Y., & Zhou, H. (2013).
*On constrained and regularized high-dimensional regression.
Annals of the Institute of Statistical Mathematics, 65(5), 807-832.*

Li, C., Shen, X., & Pan, W. (2021).
*Inference for a Large Directed Graphical Model with Interventions.
arXiv preprint arXiv:2110.03805.*

Yang, Y., & Zou, H. (2014).
*A coordinate majorization descent algorithm for l1 penalized learning.
Journal of Statistical Computation and Simulation, 84(1), 84-95.*

Two R package Github: *ncvreg* and *glmnet*.

`glmtlp`

and `plot`

, `predict`

, and `coef`

methods for `"cv.glmtlp"`

objects.

Chunlin Li, Yu Yang, Chong Wu

Maintainer: Yu Yang yang6367@umn.edu

# Gaussian X <- matrix(rnorm(100 * 20), 100, 20) y <- rnorm(100) cv.fit <- cv.glmtlp(X, y, family = "gaussian", penalty = "l1", seed=2021) # Binomial X <- matrix(rnorm(100 * 20), 100, 20) y <- sample(c(0,1), 100, replace = TRUE) cv.fit <- cv.glmtlp(X, y, family = "binomial", penalty = "l1", seed=2021)