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 anova
tidy(x, ...)
Arguments
- x
An
anova
object, such as those created bystats::anova()
,car::Anova()
,car::leveneTest()
, orcar::linearHypothesis()
.- ...
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:
Details
The term
column of an ANOVA table can come with leading or
trailing whitespace, which this tidying method trims.
For documentation on the tidier for car::leveneTest()
output, see
tidy.leveneTest()
See also
tidy()
, stats::anova()
, car::Anova()
, car::leveneTest()
Other anova tidiers:
glance.anova()
,
glance.aov()
,
tidy.TukeyHSD()
,
tidy.aovlist()
,
tidy.aov()
,
tidy.manova()
Value
A tibble::tibble()
with columns:
- df
Degrees of freedom used by this term in the model.
- meansq
Mean sum of squares. Equal to total sum of squares divided by degrees of freedom.
- 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.
- sumsq
Sum of squares explained by this term.
- term
The name of the regression term.
Examples
# fit models
a <- lm(mpg ~ wt + qsec + disp, mtcars)
b <- lm(mpg ~ wt + qsec, mtcars)
mod <- anova(a, b)
# summarize model fit with tidiers
tidy(mod)
#> # A tibble: 2 × 7
#> term df.residual rss df sumsq statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 mpg ~ wt + qsec + di… 28 195. NA NA NA NA
#> 2 mpg ~ wt + qsec 29 195. -1 -0.00102 0.000147 0.990
glance(mod)
#> # A tibble: 1 × 2
#> deviance df.residual
#> <dbl> <dbl>
#> 1 195. 29
# car::linearHypothesis() example
library(car)
mod_lht <- linearHypothesis(a, "wt - disp")
tidy(mod_lht)
#> # A tibble: 1 × 10
#> term null.value estimate std.error statistic p.value df.residual rss
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 wt - … 0 -5.03 1.23 16.6 3.39e-4 28 195.
#> # ℹ 2 more variables: df <dbl>, sumsq <dbl>
glance(mod_lht)
#> # A tibble: 1 × 2
#> deviance df.residual
#> <dbl> <dbl>
#> 1 195. 28