tidy.gam: Tidy a(n) gam object

Description

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.

Usage

# S3 method for gam
tidy(
  x,
  parametric = FALSE,
  conf.int = FALSE,
  conf.level = 0.95,
  exponentiate = FALSE,
  ...
)

Value

A tibble::tibble() with columns:

estimate: The estimated value of the regression term.
p.value: The two-sided p-value associated with the observed statistic.
statistic: The value of a T-statistic to use in a hypothesis that the regression term is non-zero.
std.error: The standard error of the regression term.
term: The name of the regression term.
edf: The effective degrees of freedom. Only reported when `parametric = FALSE`
ref.df: The reference degrees of freedom. Only reported when `parametric = FALSE`

Arguments

x

A gam object returned from a call to mgcv::gam().

parametric

Logical indicating if parametric or smooth terms should be tidied. Defaults to FALSE, meaning that smooth terms are tidied by default.

conf.int

Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to FALSE.

conf.level

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.

exponentiate

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. Two exceptions here are:

tidy() methods will warn when supplied an exponentiate argument if it will be ignored.
augment() methods will warn when supplied a newdata argument if it will be ignored.

Details

When parametric = FALSE return columns edf and ref.df rather than estimate and std.error.

Examples

Run this code


# load libraries for models and data
library(mgcv)

# fit model
g <- gam(mpg ~ s(hp) + am + qsec, data = mtcars)

# summarize model fit with tidiers
tidy(g)
tidy(g, parametric = TRUE)
glance(g)
augment(g)