R/coef_tune_xrnet.R
coef.tune_xrnet.Rd
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", ...)
object | A |
---|---|
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) |
A list with coefficient estimates at each of the requested penalty combinations
matrix of first-level intercepts indexed by penalty values, NULL if no first-level intercept in original model fit
3-dimensional array of first-level penalized coefficients indexed by penalty values
3-dimensional array of first-level non-penalized coefficients indexed by penalty values, NULL if unpen NULL in original model fit
matrix of second-level intercepts indexed by penalty values, NULL if no second-level intercept in original model fit
3-dimensional array of second-level external data coefficients indexed by penalty values, NULL if external NULL in original model fit
## 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)