R/stats-htest-tidiers.R
augment.htest.Rd
Augment accepts a model object and a dataset and adds
information about each observation in the dataset. Most commonly, this
includes predicted values in the .fitted
column, residuals in the
.resid
column, and standard errors for the fitted values in a .se.fit
column. New columns always begin with a .
prefix to avoid overwriting
columns in the original dataset.
Users may pass data to augment via either the data
argument or the
newdata
argument. If the user passes data to the data
argument,
it must be exactly the data that was used to fit the model
object. Pass datasets to newdata
to augment data that was not used
during model fitting. This still requires that at least all predictor
variable columns used to fit the model are present. If the original outcome
variable used to fit the model is not included in newdata
, then no
.resid
column will be included in the output.
Augment will often behave differently depending on whether data
or
newdata
is given. This is because there is often information
associated with training observations (such as influences or related)
measures that is not meaningfully defined for new observations.
For convenience, many augment methods provide default data
arguments,
so that augment(fit)
will return the augmented training data. In these
cases, augment tries to reconstruct the original data based on the model
object with varying degrees of success.
The augmented dataset is always returned as a tibble::tibble with the
same number of rows as the passed dataset. This means that the
passed data must be coercible to a tibble. At this time, tibbles do not
support matrix-columns. This means you should not specify a matrix
of covariates in a model formula during the original model fitting
process, and that splines::ns()
, stats::poly()
and
survival::Surv()
objects are not supported in input data. If you
encounter errors, try explicitly passing a tibble, or fitting the original
model on data in a tibble.
We are in the process of defining behaviors for models fit with various
na.action
arguments, but make no guarantees about behavior when data is
missing at this time.
# S3 method for htest
augment(x, ...)
An htest
objected, such as those created by stats::cor.test()
,
stats::t.test()
, stats::wilcox.test()
, stats::chisq.test()
, etc.
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.
See stats::chisq.test()
for more details on
how residuals are computed.
augment()
, stats::chisq.test()
Other htest tidiers:
tidy.htest()
,
tidy.pairwise.htest()
,
tidy.power.htest()
A tibble::tibble()
with exactly one row and columns:
Observed count.
Proportion of the total.
Row proportion (2 dimensions table only).
Column proportion (2 dimensions table only).
Expected count under the null hypothesis.
Pearson residuals.
Standardized residual.
tt <- t.test(rnorm(10))
tidy(tt)
#> # A tibble: 1 × 8
#> estimate statistic p.value parameter conf.low conf.high method alternative
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
#> 1 -0.177 -0.539 0.603 9 -0.918 0.565 One Sampl… two.sided
# the glance output will be the same for each of the below tests
glance(tt)
#> # A tibble: 1 × 8
#> estimate statistic p.value parameter conf.low conf.high method alternative
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
#> 1 -0.177 -0.539 0.603 9 -0.918 0.565 One Sampl… two.sided
tt <- t.test(mpg ~ am, data = mtcars)
tidy(tt)
#> # A tibble: 1 × 10
#> estimate estimate1 estimate2 statistic p.value parameter conf.low conf.high
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 -7.24 17.1 24.4 -3.77 0.00137 18.3 -11.3 -3.21
#> # … with 2 more variables: method <chr>, alternative <chr>
wt <- wilcox.test(mpg ~ am, data = mtcars, conf.int = TRUE, exact = FALSE)
tidy(wt)
#> # A tibble: 1 × 7
#> estimate statistic p.value conf.low conf.high method alternative
#> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
#> 1 -6.80 42 0.00187 -11.7 -2.90 Wilcoxon rank sum t… two.sided
ct <- cor.test(mtcars$wt, mtcars$mpg)
tidy(ct)
#> # A tibble: 1 × 8
#> estimate statistic p.value parameter conf.low conf.high method alternative
#> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <chr> <chr>
#> 1 -0.868 -9.56 1.29e-10 30 -0.934 -0.744 Pearson'… two.sided
chit <- chisq.test(xtabs(Freq ~ Sex + Class, data = as.data.frame(Titanic)))
tidy(chit)
#> # A tibble: 1 × 4
#> statistic p.value parameter method
#> <dbl> <dbl> <int> <chr>
#> 1 350. 1.56e-75 3 Pearson's Chi-squared test
augment(chit)
#> # A tibble: 8 × 9
#> Sex Class .observed .prop .row.prop .col.prop .expected .resid .std.resid
#> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Male 1st 180 0.0818 0.104 0.554 256. -4.73 -11.1
#> 2 Female 1st 145 0.0659 0.309 0.446 69.4 9.07 11.1
#> 3 Male 2nd 179 0.0813 0.103 0.628 224. -3.02 -6.99
#> 4 Female 2nd 106 0.0482 0.226 0.372 60.9 5.79 6.99
#> 5 Male 3rd 510 0.232 0.295 0.722 555. -1.92 -5.04
#> 6 Female 3rd 196 0.0891 0.417 0.278 151. 3.68 5.04
#> 7 Male Crew 862 0.392 0.498 0.974 696. 6.29 17.6
#> 8 Female Crew 23 0.0104 0.0489 0.0260 189. -12.1 -17.6