Fitting-Functions: Nine Fitting-Functions that can be used as an input of...

In XiaoruiZhu/SPSP: Selection by Partitioning the Solution Paths

Nine Fitting-Functions that can be used as an input of fitfun.SP argument to obtain the solution paths for the SPSP algorithm.

Description

The user can also customize a function to generate the solution paths, as long as the customized function take the arguments x, y, family, standardize, intercept, and return an object of class glmnet, ncvreg, and lars.

Usage

lasso.glmnet(x, y, family, standardize, intercept, ...)

lassoCV.glmnet(x, y, family, standardize, intercept, ...)

adalasso.glmnet(x, y, family, standardize, intercept, ...)

adalassoCV.glmnet(x, y, family, standardize, intercept, ...)

adalassoCVmin.glmnet(x, y, family, standardize, intercept, ...)

ridge.glmnet(x, y, family, standardize, intercept, ...)

lasso.lars(x, y, family, standardize, intercept, ...)

SCAD.ncvreg(x, y, family, standardize, intercept, ...)

MCP.ncvreg(x, y, family, standardize, intercept, ...)

Arguments

x	a matrix of the independent variables. The dimensions are (nobs) and (nvars); each row is an observation vector.
y	Response variable. Quantitative for family="gaussian" or family="poisson" (non-negative counts). For family="binomial" should be either a factor with two levels.
family	Response type. Either a character string representing one of the built-in families, or else a glm() family object.
standardize	logical argument. Should conduct standardization before the estimation? Default is TRUE.
intercept	logical. If x is a data.frame, this argument determines if the resulting model matrix should contain a separate intercept or not. Default is TRUE.
...	Additional optional arguments.

Value

An object of class "glmnet" is returned to provide solution paths for the SPSP algorithm.

An object of class "glmnet" is returned to provide solution paths for the SPSP algorithm.

An object of class "glmnet" is returned to provide solution paths for the SPSP algorithm.

An object of class "glmnet" using lambda.1se from the 10-fold cross-validation to provide solution paths for the SPSP algorithm.

An object of class "glmnet" using lambda.min from the 10-fold cross-validation to provide solution paths for the SPSP algorithm.

An object of class "glmnet" using ridge regression to provide solution paths for the SPSP algorithm.

An object of class "lars" is returned to provide solution paths for the SPSP algorithm.

An object of class "ncvreg" to provide SCAD penalty solution paths for the SPSP algorithm.

An object of class "ncvreg" to provide MCP penalty solution paths for the SPSP algorithm.

XiaoruiZhu/SPSP documentation built on Jan. 29, 2024, 5:27 a.m.