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

In USCbiostats/hierr: Hierarchical Regularized Regression

Get coefficient estimates from "tune_xrnet" model object.

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)

USCbiostats/hierr documentation built on Dec. 12, 2023, 12:55 p.m.