`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()`

.

## 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.

## 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
```