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 biglm
tidy(x, conf.int = FALSE, conf.level = 0.95, exponentiate = FALSE, ...)
A biglm
object created by a call to biglm::biglm()
or
biglm::bigglm()
.
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.
Logical indicating whether or not to exponentiate the
the coefficient estimates. This is typical for logistic and multinomial
regressions, but a bad idea if there is no log or logit link. Defaults
to FALSE
.
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.
tidy()
, biglm::biglm()
, biglm::bigglm()
Other biglm tidiers:
glance.biglm()
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("biglm", quietly = TRUE)) {
# load modeling library
library(biglm)
# fit model -- linear regression
bfit <- biglm(mpg ~ wt + disp, mtcars)
# summarize model fit with tidiers
tidy(bfit)
tidy(bfit, conf.int = TRUE)
tidy(bfit, conf.int = TRUE, conf.level = .9)
glance(bfit)
# fit model -- logistic regression
bgfit <- bigglm(am ~ mpg, mtcars, family = binomial())
# summarize model fit with tidiers
tidy(bgfit)
tidy(bgfit, exponentiate = TRUE)
tidy(bgfit, conf.int = TRUE)
tidy(bgfit, conf.int = TRUE, conf.level = .9)
tidy(bgfit, conf.int = TRUE, conf.level = .9, exponentiate = TRUE)
glance(bgfit)
}
#> # A tibble: 1 × 5
#> r.squared AIC deviance df.residual nobs
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0.175 33.7 29.7 30 32