coef.tune_xrnet: Get coefficient estimates from "tune_xrnet" model object

In xrnet: Hierarchical Regularized Regression

Description

Returns coefficients from 'xrnet' model. Note that we currently only support returning coefficient estimates that are in the original path(s).

Usage

## S3 method for class 'tune_xrnet'
coef(object, p = "opt", pext = "opt", ...)

Arguments

object	A tune_xrnet object
p	vector of penalty values to apply to predictor variables. Default is optimal value in tune_xrnet object.
pext	vector of penalty values to apply to external data variables. Default is optimal value in tune_xrnet object.
...	pass other arguments to xrnet function (if needed)

Value

A list with coefficient estimates at each of the requested penalty combinations

beta0	matrix of first-level intercepts indexed by penalty values, NULL if no first-level intercept in original model fit
betas	3-dimensional array of first-level penalized coefficients indexed by penalty values
gammas	3-dimensional array of first-level non-penalized coefficients indexed by penalty values, NULL if unpen NULL in original model fit
alpha0	matrix of second-level intercepts indexed by penalty values, NULL if no second-level intercept in original model fit
alphas	3-dimensional array of second-level external data coefficients indexed by penalty values, NULL if external NULL in original model fit

Examples

## cross validation of hierarchical linear regression model
data(GaussianExample)

## 5-fold cross validation
cv_xrnet <- tune_xrnet(
    x = x_linear,
    y = y_linear,
    external = ext_linear,
    family = "gaussian",
    control = xrnet.control(tolerance = 1e-6)
 )

 ## Get coefficient estimates at optimal penalty combination
 coef_opt <- coef(cv_xrnet)

xrnet documentation built on March 26, 2020, 9:13 p.m.