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 summary.manova
tidy(x, ...)

Arguments

x

A summary.manova object return from stats::summary.manova().

...

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.

Details

Depending on which test statistic was calculated when the object was created, only one of pillai, wilks, hl or roy is included.

See also

Value

A tibble::tibble() with columns:

den.df

Degrees of freedom of the denominator

num.df

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.

term

The name of the regression term.

pillai

Pillai's trace.

wilks

Wilk's lambda.

hl

Hotelling-Lawley trace.

roy

Roy's greatest root.

Examples

npk2 <- within(npk, foo <- rnorm(24)) m <- summary( manova(cbind(yield, foo) ~ block + N * P * K, npk2), test = "Wilks" ) tidy(m)
#> # A tibble: 8 x 7 #> term df wilks statistic num.df den.df p.value #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 block 5 0.147 3.54 10 22 0.00646 #> 2 N 1 0.491 5.70 2 11 0.0200 #> 3 P 1 0.956 0.254 2 11 0.780 #> 4 K 1 0.563 4.27 2 11 0.0424 #> 5 N:P 1 0.894 0.651 2 11 0.540 #> 6 N:K 1 0.311 12.2 2 11 0.00162 #> 7 P:K 1 0.808 1.31 2 11 0.309 #> 8 Residuals 12 NA NA NA NA NA