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

# S3 method for 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)