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 Gam
tidy(x, ...)
A Gam
object returned from a call to gam::gam()
.
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 gam
objects created by calls to mgcv::gam()
with
tidy.gam()
.
tidy()
, gam::gam()
, tidy.anova()
, tidy.gam()
Other gam tidiers:
glance.Gam()
A tibble::tibble()
with columns:
Degrees of freedom used by this term in the model.
Mean sum of squares. Equal to total sum of squares divided by degrees of freedom.
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.
Sum of squares explained by this 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("gam", quietly = TRUE)) {
# load libraries for models and data
library(gam)
# fit model
g <- gam(mpg ~ s(hp, 4) + am + qsec, data = mtcars)
# summarize model fit with tidiers
tidy(g)
glance(g)
}
#> Loading required package: splines
#> Loading required package: foreach
#>
#> Attaching package: ‘foreach’
#> The following objects are masked from ‘package:purrr’:
#>
#> accumulate, when
#> Loaded gam 1.20.1
#>
#> Attaching package: ‘gam’
#> The following objects are masked from ‘package:mgcv’:
#>
#> gam, gam.control, gam.fit, s
#> # A tibble: 1 × 7
#> df logLik AIC BIC deviance df.residual nobs
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 7.00 -76.0 162. 169. 180. 25.0 32