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 cross 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, ...)



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.


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.


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

To tidy Gam objects created by calls to gam::gam(), see tidy.Gam().

See also

tidy(), mgcv::gam(), tidy.Gam()

Other mgcv tidiers: glance.gam


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.


g <- mgcv::gam(mpg ~ s(hp) + am + qsec, data = mtcars) tidy(g)
#> # A tibble: 1 x 5 #> term edf ref.df statistic p.value #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 s(hp) 2.36 3.02 6.34 0.00207
tidy(g, parametric = TRUE)
#> # A tibble: 3 x 5 #> term estimate std.error statistic p.value #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 (Intercept) 16.7 9.83 1.70 0.101 #> 2 am 4.37 1.56 2.81 0.00918 #> 3 qsec 0.0904 0.525 0.172 0.865
#> # A tibble: 1 x 6 #> df logLik AIC BIC deviance df.residual #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 5.36 -74.4 162. 171. 196. 26.6