Main fitting function for group lasso and cooperative lasso penalized two part models

This function fits penalized two part models with a logistic regression model for the zero part and a gamma regression model for the positive part. Each covariate's effect has either a group lasso or cooperative lasso penalty for its effects for the two consituent models

hd2part(
  x,
  z,
  x_s,
  s,
  weights = rep(1, NROW(x)),
  weights_s = rep(1, NROW(x_s)),
  offset = NULL,
  offset_s = NULL,
  penalty = c("grp.lasso", "coop.lasso"),
  penalty_factor = NULL,
  nlambda = 100L,
  lambda_min_ratio = ifelse(n_s < p, 0.05, 0.005),
  lambda = NULL,
  tau = 0,
  opposite_signs = FALSE,
  flip_beta_zero = FALSE,
  intercept_z = FALSE,
  intercept_s = FALSE,
  strongrule = TRUE,
  maxit_irls = 50,
  tol_irls = 1e-05,
  maxit_mm = 500,
  tol_mm = 1e-05,
  balance_likelihoods = TRUE
)

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
z	a length n vector of responses taking values 1 and 0, where 1 indicates the response is positive and zero indicates the response has value 0.
x_s	an n_s x p matrix of covariates (which is a submatrix of x) for the positive part data, where each row is an observation and each column is a predictor
s	a length n_s vector of responses taking strictly positive values
weights	a length n vector of observation weights for the zero part data
weights_s	a length n_s vector of observation weights for the positive part data
offset	a length n vector of offset terms for the zero part data
offset_s	a length n_s vector of offset terms for the positive part data
penalty	either "grp.lasso" for the group lasso penalty or "coop.lasso" for the cooperative lasso penalty
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	value between 0 and 1 for sparse group mixing penalty. 0 implies either group lasso or coop lasso and 1 implies lasso
opposite_signs	a boolean variable indicating whether the signs of coefficients across models should be encouraged to have opposite signs instead of the same signs. Default is FALSE. This variable has no effect for group lasso.
flip_beta_zero	should we flip the signs of the parameters for the zero part model? Defaults to FALSE. Should only be used for good reason
intercept_z	whether or not to include an intercept in the zero part model. Default is TRUE.
intercept_s	whether or not to include an intercept in the positive part model. Default is TRUE.
strongrule	should a strong rule be used? Defaults to TRUE
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.
balance_likelihoods	should the likelihoods be balanced so variables would enter both models at the same value of lambda if the penalty were a lasso penalty? Recommended to keep at the default, TRUE

Examples

library(personalized2part)