gilez: Substantive effects via simulation

In deepfriar/gilez: Substantive estimates via simulation

Description

Substantive effects via simulation

Usage

gilez(m, x = stats::model.frame(m), Z = bestz(m, x), f = value,
  y = 0, n = 1000, parallel = FALSE, w = getweights(m, x),
  g = stats::weighted.mean, ...)

Arguments

m	a model.
x	a population within which to simulate effects. Default model.frame(m) (the original population).
Z	a named ist of two or more contrasting values for each variable. Default bestz(m, x).
f	a function for boiling down results. This is a function, not a string. Default value.
y	values of the outcome representing success under f. Default 0.
n	number of simulated response vectors to generate for each level for each test. Default 1000.
parallel	logical. Passed on to ddply at the outermost (term and level) levels.
w	weights with respect to which to take averages. Default getweights(m, x).
g	function. the desired one-number summary of the boiled values. Default stats::weighted.mean.
...	other arguments to functions used within.

Value

a tall data.frame with class gilez to pass to gdiff or sumer.

deepfriar/gilez documentation built on Dec. 19, 2021, 10:09 p.m.