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, ...)

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.

...

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.

Details

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

See also

tidy(), mgcv::gam()

Other mgcv tidiers: glance.gam()

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`

Examples

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 0.00218
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
glance(g)
#> # A tibble: 1 x 7 #> df logLik AIC BIC deviance df.residual nobs #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> #> 1 5.36 -74.4 162. 171. 196. 26.6 32
#> # A tibble: 32 x 11 #> .rownames mpg am qsec hp .fitted .se.fit .resid .hat .sigma #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> #> 1 Mazda RX4 21 1 16.5 110 24.3 1.03 -3.25 0.145 NA #> 2 Mazda RX… 21 1 17.0 110 24.3 0.925 -3.30 0.116 NA #> 3 Datsun 7… 22.8 1 18.6 93 26.0 0.894 -3.22 0.109 NA #> 4 Hornet 4… 21.4 0 19.4 110 20.2 0.827 1.25 0.0930 NA #> 5 Hornet S… 18.7 0 17.0 175 15.7 0.815 3.02 0.0902 NA #> 6 Valiant 18.1 0 20.2 105 20.7 0.914 -2.56 0.113 NA #> 7 Duster 3… 14.3 0 15.8 245 12.7 1.11 1.63 0.167 NA #> 8 Merc 240D 24.4 0 20 62 25.0 1.45 -0.618 0.287 NA #> 9 Merc 230 22.8 0 22.9 95 21.8 1.81 0.959 0.446 NA #> 10 Merc 280 19.2 0 18.3 123 19.0 0.864 0.211 0.102 NA #> # … with 22 more rows, and 1 more variable: .cooksd <dbl>