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.
# S3 method for mle2
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)
An mle2
object created by a call to bbmle::mle2()
.
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.
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 columns:
Upper bound on the confidence interval for the estimate.
Lower bound on the confidence interval for the estimate.
The estimated value of the regression term.
The two-sided p-value associated with the observed statistic.
The value of a T-statistic to use in a hypothesis that the regression term is non-zero.
The standard error of the regression term.
The name of the regression term.
# feel free to ignore the following line—it allows {broom} to supply
# examples without requiring the model-supplying package to be installed.
if (requireNamespace("bbmle", quietly = TRUE)) {
# load libraries for models and data
library(bbmle)
# generate data
x <- 0:10
y <- c(26, 17, 13, 12, 20, 5, 9, 8, 5, 4, 8)
d <- data.frame(x, y)
# fit model
fit <- mle2(y ~ dpois(lambda = ymean),
start = list(ymean = mean(y)), data = d
)
# summarize model fit with tidiers
tidy(fit)
}
#> Loading required package: stats4
#>
#> Attaching package: ‘bbmle’
#> The following object is masked from ‘package:dfidx’:
#>
#> slice
#> The following object is masked from ‘package:ordinal’:
#>
#> slice
#> The following object is masked from ‘package:dplyr’:
#>
#> slice
#> # A tibble: 1 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 ymean 11.5 1.02 11.3 1.86e-29