Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.
A boot::boot()
object.
Logical indicating whether or not to include a confidence
interval in the tidied output. Defaults to FALSE
.
The confidence level to use for the confidence interval
if conf.int = TRUE
. Must be strictly greater than 0 and less than 1.
Defaults to 0.95, which corresponds to a 95 percent confidence interval.
Passed to the type
argument of boot::boot.ci()
.
Defaults to "perc"
. The allowed types are "perc"
, "basic"
,
"bca"
, and "norm"
. Does not support "stud"
or "all"
.
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in ...
, where they will be ignored. If the misspelled
argument has a default value, the default value will be used.
For example, if you pass conf.lvel = 0.9
, all computation will
proceed using conf.level = 0.95
. Additionally, if you pass
newdata = my_tibble
to an augment()
method that does not
accept a newdata
argument, it will use the default value for
the data
argument.
If weights were provided to the boot
function, an estimate
column is included showing the weighted bootstrap estimate, and the
standard error is of that estimate.
If there are no original statistics in the "boot" object, such as with a
call to tsboot
with orig.t = FALSE
, the original
and statistic
columns are omitted, and only estimate
and
std.error
columns shown.
A tibble::tibble()
with columns:
Bias of the statistic.
The standard error of the regression term.
The name of the regression term.
Original value of the statistic.
# feel free to ignore the following line—it allows {broom} to supply
# examples without requiring the model-supplying package to be installed.
if (requireNamespace("boot", quietly = TRUE)) {
# load modeling library
library(boot)
clotting <- data.frame(
u = c(5, 10, 15, 20, 30, 40, 60, 80, 100),
lot1 = c(118, 58, 42, 35, 27, 25, 21, 19, 18),
lot2 = c(69, 35, 26, 21, 18, 16, 13, 12, 12)
)
# fit models
g1 <- glm(lot2 ~ log(u), data = clotting, family = Gamma)
bootfun <- function(d, i) {
coef(update(g1, data = d[i, ]))
}
bootres <- boot(clotting, bootfun, R = 999)
# summarize model fits with tidiers
tidy(g1, conf.int = TRUE)
tidy(bootres, conf.int = TRUE)
}
#>
#> Attaching package: ‘boot’
#> The following object is masked from ‘package:speedglm’:
#>
#> control
#> The following object is masked from ‘package:robustbase’:
#>
#> salinity
#> The following object is masked from ‘package:car’:
#>
#> logit
#> The following object is masked from ‘package:survival’:
#>
#> aml
#> # A tibble: 2 × 6
#> term statistic bias std.error conf.low conf.high
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) -0.0239 -0.00171 0.00336 -0.0328 -0.0222
#> 2 log(u) 0.0236 0.000504 0.00107 0.0227 0.0265