Tidy a(n) Gam objectSource:
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, ...)
Gamobject returned from a call to
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:
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.
# load libraries for models and data library(gam) #> Loading required package: splines #> Loading required package: foreach #> #> Attaching package: ‘foreach’ #> The following objects are masked from ‘package:purrr’: #> #> accumulate, when #> Loaded gam 1.22 #> #> Attaching package: ‘gam’ #> The following objects are masked from ‘package:mgcv’: #> #> gam, gam.control, gam.fit, s # fit model g <- gam(mpg ~ s(hp, 4) + am + qsec, data = mtcars) # summarize model fit with tidiers tidy(g) #> # A tibble: 4 × 6 #> term df sumsq meansq statistic p.value #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 s(hp, 4) 1 678. 678. 94.4 5.73e-10 #> 2 am 1 113. 113. 15.7 5.52e- 4 #> 3 qsec 1 0.0263 0.0263 0.00366 9.52e- 1 #> 4 Residuals 25.0 180. 7.19 NA NA glance(g) #> # 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