summary.fwb {fwb}

Summarize fwb Output

Description

summary() creates a regression summary-like table that displays the bootstrap estimates, their empirical standard errors, and their confidence intervals, which are computed using fwb.ci().

Usage

## S3 method for class 'fwb'
summary(
  object,
  conf = 0.95,
  ci.type = "bc",
  p.value = FALSE,
  index = 1L:ncol(object$t),
  ...
)

Arguments

object

an fwb object; the output of a call to fwb().

conf

the desired confidence level. Default is .95 for 95% confidence intervals.

ci.type

the type of confidence interval desired. Allowable options include "norm" (normal approximation), "basic" (basic interval), "perc" (percentile interval), "bc" (bias-correct percentile interval), and "bca" (bias-corrected and accelerated [BCa] interval). Only one is allowed. BCa intervals require that the number of bootstrap replications is larger than the sample size. See fwb.ci() for details. The default is "bc".

p.value

logical; whether to display p-values for the test that each parameter is equal to 0. The p-value is computed using a Z-test with the test statistic computed as the ratio of the estimate to its bootstrap standard error. This test is only valid when the bootstrap distribution is normally distributed around 0 and is not guaranteed to agree with any of the confidence intervals. Default is FALSE.

index

the index or indices of the position of the quantity of interest in x$t0 if more than one was specified in fwb(). Default is to display all quantities.

...

ignored.

Value

A summary.fwb object, which is a matrix with the following columns:

• Estimate: the statistic estimated in the original sample

• ⁠Std. Error⁠: the standard deviation of the bootstrap estimates

• ⁠CI {L}%⁠ and ⁠CI {U}%⁠, the upper and lower confidence interval bounds computed using the argument to ci.type.

When p.value = TRUE, two additional columns, ⁠z value⁠ and ⁠Pr(>|z|)⁠ are included containing the z-statistic and p-value for each computed statistic.

See Also

fwb() for performing the fractional weighted bootstrap; fwb.ci() for computing multiple confidence intervals for a single bootstrapped quantity

Examples

set.seed(123)
data("infert")

fit_fun <- function(data, w) {
  fit <- glm(case ~ spontaneous + induced, data = data,
             family = "quasibinomial", weights = w)
  coef(fit)
}

fwb_out <- fwb(infert, fit_fun, R = 199, verbose = FALSE)

# Basic confidence interval for both estimates
summary(fwb_out, ci.type = "basic")

# Just for "induced" coefficient; p-values requested
summary(fwb_out, index = "induced", p.value = TRUE)