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 class 'gam'
tidy(
x,
parametric = FALSE,
conf.int = FALSE,
conf.level = 0.95,
exponentiate = FALSE,
...
)Arguments
- x
A
gamobject returned from a call tomgcv::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 passconf.lvel = 0.9, all computation will proceed usingconf.level = 0.95. Two exceptions here are:
See also
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
# 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)
#> # A tibble: 1 × 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 × 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 × 9
#> df logLik AIC BIC deviance df.residual nobs adj.r.squared npar
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <int>
#> 1 5.36 -74.4 162. 171. 196. 26.6 32 0.797 12
augment(g)
#> # A tibble: 32 × 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 RX4… 21 1 17.0 110 24.3 0.925 -3.30 0.116 NA
#> 3 Datsun 710 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 Sp… 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 360 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
#> # ℹ 22 more rows
#> # ℹ 1 more variable: .cooksd <dbl>