Summarize fwb Output

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")
#>             Estimate Std. Error CI 2.5 % CI 97.5 %
#> (Intercept)  -1.7079     0.2566  -2.1334   -1.1875
#> spontaneous   1.1972     0.2036   0.7023    1.5613
#> induced       0.4181     0.1823   0.0471    0.7979

# Just for "induced" coefficient; p-values requested
summary(fwb_out, index = "induced", p.value = TRUE)
#>         Estimate Std. Error CI 2.5 % CI 97.5 % z value Pr(>|z|)  
#> induced   0.4181     0.1823   0.0539    0.8536    2.29    0.022 *
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1