hdgamma: Fitting function for lasso penalized gamma GLMs

In jaredhuling/personalized2part: Two-Part Estimation of Treatment Rules for Semi-Continuous Data

Description

This function fits penalized gamma GLMs

Usage

hdgamma(
  x,
  y,
  weights = rep(1, NROW(x)),
  offset = NULL,
  penalty_factor = NULL,
  nlambda = 100L,
  lambda_min_ratio = ifelse(n < p, 0.05, 0.005),
  lambda = NULL,
  tau = 0,
  intercept = TRUE,
  strongrule = TRUE,
  maxit_irls = 50,
  tol_irls = 1e-05,
  maxit_mm = 500,
  tol_mm = 1e-05
)

Arguments

x	an n x p matrix of covariates for the zero part data, where each row is an observation and each column is a predictor
y	a length n vector of responses taking strictly positive values.
weights	a length n vector of observation weights
offset	a length n vector of offset terms
penalty_factor	a length p vector of penalty adjustment factors corresponding to each covariate. A value of 0 in the jth location indicates no penalization on the jth variable, and any positive value will indicate a multiplicative factor on top of the common penalization amount. The default value is 1 for all variables
nlambda	the number of lambda values. The default is 100.
lambda_min_ratio	Smallest value for lambda, as a fraction of lambda.max, the data-derived largest lambda value The default depends on the sample size relative to the number of variables.
lambda	a user supplied sequence of penalization tuning parameters. By default, the program automatically chooses a sequence of lambda values based on nlambda and lambda_min_ratio
tau	a scalar numeric value between 0 and 1 (included) which is a mixing parameter for sparse group lasso penalty. 0 indicates group lasso and 1 indicates lasso, values in between reflect different emphasis on group and lasso penalties
intercept	whether or not to include an intercept. Default is TRUE.
strongrule	should a strong rule be used?
maxit_irls	maximum number of IRLS iterations
tol_irls	convergence tolerance for IRLS iterations
maxit_mm	maximum number of MM iterations. Note that for algorithm = "irls", MM is used within each IRLS iteration, so maxit_mm applies to the convergence of the inner iterations in this case.
tol_mm	convergence tolerance for MM iterations. Note that for algorithm = "irls", MM is used within each IRLS iteration, so tol_mm applies to the convergence of the inner iterations in this case.

Examples

library(personalized2part)

jaredhuling/personalized2part documentation built on Sept. 16, 2020, 1:41 p.m.