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 cross models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.
# S3 method for boot
tidy(x, conf.int = FALSE, conf.level = 0.95, conf.method = "perc", ...)
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"
.
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.
A tibble::tibble with one row per bootstrapped statistic and columns:
Name of the computed statistic, if present.
Original value of the statistic.
Bias of the statistic.
Standard error of the statistic.
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.
tidy()
, boot::boot()
, boot::tsboot()
, boot::boot.ci()
,
rsample::bootstraps()
# NOT RUN {
if (require("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))
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)
tidy(g1, conf.int=TRUE)
tidy(bootres, conf.int=TRUE)
}
# }
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