Augment data with information from a(n) htest object

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. If a predictor enters the model as part of a matrix of covariates, such as when the model formula uses splines::ns(), stats::poly(), or survival::Surv(), it is represented as a matrix column.

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.

Usage

# S3 method for htest
augment(x, ...)

Arguments

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. Two exceptions here are:

tidy() methods will warn when supplied an exponentiate argument if it will be ignored.
augment() methods will warn when supplied a newdata argument if it will be ignored.

Details

See stats::chisq.test() for more details on how residuals are computed.

Value

A tibble::tibble() with exactly one row and columns:

.observed: Observed count.
.prop: Proportion of the total.
.row.prop: Row proportion (2 dimensions table only).
.col.prop: Column proportion (2 dimensions table only).
.expected: Expected count under the null hypothesis.
.resid: Pearson residuals.
.std.resid: Standardized residual.

Examples


tt <- t.test(rnorm(10))

tidy(tt)
#> # A tibble: 1 × 8
#>   estimate statistic p.value parameter conf.low conf.high method          
#>      <dbl>     <dbl>   <dbl>     <dbl>    <dbl>     <dbl> <chr>           
#> 1   -0.177    -0.539   0.603         9   -0.918     0.565 One Sample t-te…
#> # ℹ 1 more variable: alternative <chr>

# 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          
#>      <dbl>     <dbl>   <dbl>     <dbl>    <dbl>     <dbl> <chr>           
#> 1   -0.177    -0.539   0.603         9   -0.918     0.565 One Sample t-te…
#> # ℹ 1 more variable: alternative <chr>

tt <- t.test(mpg ~ am, data = mtcars)

tidy(tt)
#> # A tibble: 1 × 10
#>   estimate estimate1 estimate2 statistic p.value parameter conf.low
#>      <dbl>     <dbl>     <dbl>     <dbl>   <dbl>     <dbl>    <dbl>
#> 1    -7.24      17.1      24.4     -3.77 0.00137      18.3    -11.3
#> # ℹ 3 more variables: conf.high <dbl>, 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… 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         
#>      <dbl>     <dbl>    <dbl>     <int>    <dbl>     <dbl> <chr>          
#> 1   -0.868     -9.56 1.29e-10        30   -0.934    -0.744 Pearson's prod…
#> # ℹ 1 more variable: alternative <chr>

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
#>   <fct>  <fct>     <dbl>  <dbl>     <dbl>     <dbl>     <dbl>  <dbl>
#> 1 Male   1st         180 0.0818    0.104     0.554      256.   -4.73
#> 2 Female 1st         145 0.0659    0.309     0.446       69.4   9.07
#> 3 Male   2nd         179 0.0813    0.103     0.628      224.   -3.02
#> 4 Female 2nd         106 0.0482    0.226     0.372       60.9   5.79
#> 5 Male   3rd         510 0.232     0.295     0.722      555.   -1.92
#> 6 Female 3rd         196 0.0891    0.417     0.278      151.    3.68
#> 7 Male   Crew        862 0.392     0.498     0.974      696.    6.29
#> 8 Female Crew         23 0.0104    0.0489    0.0260     189.  -12.1 
#> # ℹ 1 more variable: .std.resid <dbl>