control_sel: Control parameters for the selection model

In nonprobsvy: Inference Based on Non-Probability Samples

Control parameters for the selection model

Description

control_sel constructs a list with all necessary control parameters for selection model.

Usage

control_sel(
  est_method = c("mle", "gee"),
  gee_h_fun = 1,
  optimizer = c("maxLik", "optim"),
  maxlik_method = c("NR", "BFGS", "NM"),
  optim_method = c("BFGS", "Nelder-Mead"),
  epsilon = 1e-04,
  maxit = 500,
  trace = FALSE,
  penalty = c("SCAD", "lasso", "MCP"),
  a_SCAD = 3.7,
  a_MCP = 3,
  lambda = -1,
  lambda_min = 0.001,
  nlambda = 50,
  nfolds = 10,
  print_level = 0,
  start_type = c("zero", "mle", "naive"),
  nleqslv_method = c("Broyden", "Newton"),
  nleqslv_global = c("dbldog", "pwldog", "cline", "qline", "gline", "hook", "none"),
  nleqslv_xscalm = c("fixed", "auto"),
  dependence = FALSE,
  key = NULL
)

Arguments

est_method	Method of estimation for propensity score model ("mle" or "gee"; default is "mle").
gee_h_fun	Smooth function for the generalized estimating equations (GEE) method.
optimizer	(for the est_method="mle" only) optimization function for maximum likelihood estimation.
maxlik_method	(for the est_method="mle" only) maximisation method that will be passed to maxLik::maxLik() function. Default is NR.
optim_method	(for the est_method="mle" only) maximisation method that will be passed to stats::optim() function. Default is BFGS.
epsilon	Tolerance for fitting algorithms by default 1e-6.
maxit	Maximum number of iterations.
trace	logical value. If TRUE trace steps of the fitting algorithms. Default is FALSE
penalty	The penalization function used during variables selection.
a_SCAD	The tuning parameter of the SCAD penalty for selection model. Default is 3.7.
a_MCP	The tuning parameter of the MCP penalty for selection model. Default is 3.
lambda	A user-specified \lambda value during variable selection model fitting.
lambda_min	The smallest value for lambda, as a fraction of lambda.max. Default is .001.
nlambda	The number of lambda values. Default is 50.
nfolds	The number of folds for cross validation. Default is 10.
print_level	this argument determines the level of printing which is done during the optimization (for propensity score model) process.
start_type	Type of method for start points for model fitting taking the following values if zero then start is a vector of zeros (default for all methods). if mle (for est_method="gee" only) starting parameters are taken from the result of the est_method="mle" method.
nleqslv_method	(for the est_method="gee" only) The method that will be passed to nleqslv::nleqslv() function.
nleqslv_global	(for the est_method="gee" only) The global strategy that will be passed to nleqslv::nleqslv() function.
nleqslv_xscalm	(for the est_method="gee" only) The type of x scaling that will be passed to nleqslv::nleqslv() function.
dependence	logical value (default TRUE) informing whether samples overlap (NOT YET IMPLEMENTED, FOR FUTURE DEVELOPMENT).
key	binary key variable allowing to identify the overlap (NOT YET IMPLEMENTED, FOR FUTURE DEVELOPMENT).

Details

Smooth function (gee_h_fun) for the generalized estimating equations (GEE) method taking the following values

• if 1 then \boldsymbol{h}\left(\boldsymbol{x}, \boldsymbol{\theta}\right) = \frac{\pi(\boldsymbol{x}, \boldsymbol{\theta})}{\boldsymbol{x}},

• if 2 then \boldsymbol{h}\left(\boldsymbol{x}, \boldsymbol{\theta}\right) = \boldsymbol{x}

Value

List with selected parameters.

See Also

nonprob() – for fitting procedure with non-probability samples.

nonprobsvy documentation built on April 3, 2025, 7:08 p.m.