Get the tidy variance summary from a fitted OLS maars_lm, lm class object

Get the tidy variance summary from a fitted OLS maars_lm, lm class object

get_summary(
  mod_fit,
  sand = NULL,
  boot_emp = NULL,
  boot_sub = NULL,
  boot_mul = NULL,
  boot_res = NULL,
  well_specified = NULL
)

Arguments

mod_fit	(`maars_lm, lm`) A fitted OLS `maars_lm, lm` class object
sand	(logical) : `TRUE` if sandwich estimator output is required, `FALSE` to exclude this output from the request
boot_emp	(logical) : `TRUE` if empirical bootstrap standard error output is required, `FALSE` to exclude this output from the request
boot_sub	(logical) : `TRUE` if subsampling standard error output is required, `FALSE` to exclude this output from the request
boot_mul	(logical) : `TRUE` if multiplier bootstrap standard error output is required, `FALSE` to exclude this output from the request
boot_res	(logical) : `TRUE` if residual bootstrap standard error output is required, `FALSE` to exclude this output from the request
well_specified	(logical) : `TRUE` if lm standard errors (well specified) output is required, `FALSE` to exclude this output from the request

Value

(tibble) : Combined standard error summary from a fitted OLS maars_lm, lm class object

Examples

if (FALSE) {
set.seed(1243434)

# generate data
n <- 1e3
X_1 <- stats::rnorm(n, 0, 1)
X_2 <- stats::rnorm(n, 10, 20)
eps <- stats::rnorm(n, 0, 1)

# OLS data and model
y <- 2 + X_1 * 1 + X_2 * 5 + eps
lm_fit <- stats::lm(y ~ X_1 + X_2)


# Empirical Bootstrap check
set.seed(454354534)
mms_var <- comp_var(
  mod_fit = lm_fit, boot_emp = list(B = 20, m = 200),
  boot_res = list(B = 30)
)

get_summary(mms_var)

# compute variance with multiplier bootstrap now
mms_var2 <- comp_var(
  mod_fit = lm_fit,
  boot_mul = list(B = 100)
)

get_summary(mms_var2)

}