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, parametric = FALSE, conf.int = FALSE, conf.level = 0.95, ...)A gam object returned from a call to mgcv::gam().
Logical indicating if parametric or smooth terms should
be tidied. Defaults to FALSE, meaning that smooth terms are tidied
by default.
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.level = 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:
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.
The effective degrees of freedom. Only reported when `parametric = FALSE`
The reference degrees of freedom. Only reported when `parametric = FALSE`
When parametric = FALSE return columns edf and ref.df rather
than estimate and std.error.
Other mgcv tidiers:
glance.gam()
# NOT RUN {
g <- mgcv::gam(mpg ~ s(hp) + am + qsec, data = mtcars)
tidy(g)
tidy(g, parametric = TRUE)
glance(g)
# }
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