hdgamma {personalized2part} | R Documentation |
Fitting function for lasso penalized GLMs
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 |
a user supplied sequence of penalization tuning parameters. By default, the program automatically
chooses a sequence of lambda values based on |
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 |
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 |
tol_mm |
convergence tolerance for MM iterations. Note that for |
Examples
library(personalized2part)