slipper_lm_: Bootstrap a linear regression model

In jtleek/slipper: Easy and tidy bootstrapping

Description

Takes a data frame, and a model to fit to the data and each bootstrap replicate. Bootstrapping is by default resampling cases, but if you set boot_resid=TRUE then resampling residuals will be performed. If you pass a null model formula that includes a subset of the variables in the full model (i.e. it is a nested model) then the bootstrap statistics will come from the bootstrapped null data and can be used for a hypothesis test.

Usage

slipper_lm_(df, formula, null_formula = NULL, B = 100, boot_resid = FALSE)

Arguments

df	A data frame
formula	A an expression for a formula to pass to the lm command
null_formula	(optional) If NULL, standard bootstrapping is performed. If a nested expression for a null formula is passed the bootstrapped statistics come from the null.
B	the number of bootstrap samples to draw
boot_resid	If TRUE then bootstrapping residuals is performed.

Value

out A data frame with the values, whether they come from the observed data or the bootstrapped data, and the coefficient name.

jtleek/slipper documentation built on May 20, 2019, 3:14 a.m.