xrnet.control: Control function for xrnet fitting

In xrnet: Hierarchical Regularized Regression

Description

Control function for xrnet fitting.

Usage

xrnet.control(
  tolerance = 1e-08,
  max_iterations = 1e+05,
  dfmax = NULL,
  pmax = NULL,
  lower_limits = NULL,
  upper_limits = NULL
)

Arguments

tolerance	positive convergence criterion. Default is 1e-08.
max_iterations	maximum number of iterations to run coordinate gradient descent across all penalties before returning an error. Default is 1e+05.
dfmax	maximum number of variables allowed in model. Default is ncol(x) + ncol(unpen) + ncol(external) + intercept[1] + intercept[2].
pmax	maximum number of variables with nonzero coefficient estimate. Default is min(2 * dfmax + 20, ncol(x) + ncol(unpen) + ncol(external) + intercept[2]).
lower_limits	vector of lower limits for each coefficient. Default is -Inf for all variables.
upper_limits	vector of upper limits for each coefficient. Default is Inf for all variables.

Value

A list object with the following components:

tolerance	The coordinate descent stopping criterion.
dfmax	The maximum number of variables that will be allowed in the model.
pmax	The maximum number of variables with nonzero coefficient estimate.
lower_limits	Feature-specific numeric vector of lower bounds for coefficient estimates
upper_limits	Feature-specific numeric vector of upper bounds for coefficient estimates

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