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 speedlm
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)
A speedlm
object returned from speedglm::speedlm()
.
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.
speedglm::speedlm()
, tidy.lm()
Other speedlm tidiers:
augment.speedlm()
,
glance.speedglm()
,
glance.speedlm()
,
tidy.speedglm()
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("speedglm", quietly = TRUE)) {
# load modeling library
library(speedglm)
# fit model
mod <- speedlm(mpg ~ wt + qsec, data = mtcars, fitted = TRUE)
# summarize model fit with tidiers
tidy(mod)
glance(mod)
augment(mod)
}
#> # A tibble: 32 × 6
#> .rownames mpg wt qsec .fitted .resid
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Mazda RX4 21 2.62 16.5 21.8 -0.815
#> 2 Mazda RX4 Wag 21 2.88 17.0 21.0 -0.0482
#> 3 Datsun 710 22.8 2.32 18.6 25.3 -2.53
#> 4 Hornet 4 Drive 21.4 3.22 19.4 21.6 -0.181
#> 5 Hornet Sportabout 18.7 3.44 17.0 18.2 0.504
#> 6 Valiant 18.1 3.46 20.2 21.1 -2.97
#> 7 Duster 360 14.3 3.57 15.8 16.4 -2.14
#> 8 Merc 240D 24.4 3.19 20 22.2 2.17
#> 9 Merc 230 22.8 3.15 22.9 25.1 -2.32
#> 10 Merc 280 19.2 3.44 18.3 19.4 -0.185
#> # … with 22 more rows