control_inf constructs a list with all necessary control parameters
for statistical inference.
control_inf(
var_method = c("analytic", "bootstrap"),
rep_type = c("subbootstrap", "auto", "JK1", "JKn", "BRR", "bootstrap", "mrbbootstrap",
"Fay"),
vars_selection = FALSE,
vars_combine = FALSE,
bias_correction = FALSE,
num_boot = 500,
alpha = 0.05,
cores = 1,
keep_boot = TRUE,
nn_exact_se = FALSE
)A list with selected parameters.
the variance method (default "analytic").
the replication type for weights in the bootstrap method for variance estimation passed to survey::as.svrepdesign().
Default is "subbootstrap".
default FALSE; if TRUE, then the variables selection model is used.
whether variables should be combined after variable selection for doubly robust estimators (default FALSE)
default FALSE; if TRUE, then the bias minimization estimation used during model fitting.
the number of iteration for bootstrap algorithms.
significance level (default 0.05).
the number of cores in parallel computing (default 1).
a logical value indicating whether statistics from bootstrap should be kept (default TRUE)
a logical value indicating whether to compute the exact
standard error estimate for nn or pmm estimator. The variance estimator for
estimation based on nn or pmm can be decomposed into three parts, with the
third computed using covariance between imputed values for units in
the probability sample using predictive matches from the non-probability sample.
In most situations this term is negligible and is very computationally
expensive so by default it is set to FALSE, but the recommended option is to
set this value to TRUE before submitting the final results.
nonprob() -- for fitting procedure with non-probability samples.