Augment data with information from a(n) polr 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. 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 polr
augment(
  x,
  data = model.frame(x),
  newdata = NULL,
  type.predict = c("class"),
  ...
)

Arguments

x	A `polr` object returned from `MASS::polr()`.
data	A base::data.frame or `tibble::tibble()` containing the original data that was used to produce the object `x`. Defaults to `stats::model.frame(x)` so that `augment(my_fit)` returns the augmented original data. Do not pass new data to the `data` argument. Augment will report information such as influence and cooks distance for data passed to the `data` argument. These measures are only defined for the original training data.
newdata	A `base::data.frame()` or `tibble::tibble()` containing all the original predictors used to create `x`. Defaults to `NULL`, indicating that nothing has been passed to `newdata`. If `newdata` is specified, the `data` argument will be ignored.
type.predict	Which type of prediction to compute, passed to `MASS:::predict.polr()`. Only supports `"class"` at the moment.
...	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.

Examples


library(MASS)

fit <- polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing)

tidy(fit, exponentiate = TRUE, conf.int = TRUE)
#> 
#> Re-fitting to get Hessian
#> # A tibble: 8 x 7
#>   term          estimate std.error statistic conf.low conf.high coef.type  
#>   <chr>            <dbl>     <dbl>     <dbl>    <dbl>     <dbl> <chr>      
#> 1 InflMedium       1.76     0.105       5.41    1.44      2.16  coefficient
#> 2 InflHigh         3.63     0.127      10.1     2.83      4.66  coefficient
#> 3 TypeApartment    0.564    0.119      -4.80    0.446     0.712 coefficient
#> 4 TypeAtrium       0.693    0.155      -2.36    0.511     0.940 coefficient
#> 5 TypeTerrace      0.336    0.151      -7.20    0.249     0.451 coefficient
#> 6 ContHigh         1.43     0.0955      3.77    1.19      1.73  coefficient
#> 7 Low|Medium       0.609    0.125      -3.97   NA        NA     scale      
#> 8 Medium|High      2.00     0.125       5.50   NA        NA     scale      

glance(fit)
#> # A tibble: 1 x 7
#>     edf logLik   AIC   BIC deviance df.residual  nobs
#>   <int>  <dbl> <dbl> <dbl>    <dbl>       <int> <int>
#> 1     8 -1740. 3495. 3539.    3479.        1673  1681
augment(fit, type.predict = "class")
#> # A tibble: 72 x 6
#>    Sat    Infl   Type      Cont  `(weights)` .fitted
#>    <ord>  <fct>  <fct>     <fct>       <int> <fct>  
#>  1 Low    Low    Tower     Low            21 Low    
#>  2 Medium Low    Tower     Low            21 Low    
#>  3 High   Low    Tower     Low            28 Low    
#>  4 Low    Medium Tower     Low            34 High   
#>  5 Medium Medium Tower     Low            22 High   
#>  6 High   Medium Tower     Low            36 High   
#>  7 Low    High   Tower     Low            10 High   
#>  8 Medium High   Tower     Low            11 High   
#>  9 High   High   Tower     Low            36 High   
#> 10 Low    Low    Apartment Low            61 Low    
#> # … with 62 more rows

fit2 <- polr(factor(gear) ~ am + mpg + qsec, data = mtcars)
tidy(fit, p.values = TRUE)
#> 
#> Re-fitting to get Hessian
#> p-values can presently only be returned for models that contain 
#>               no categorical variables with more than two levels
#> # A tibble: 8 x 6
#>   term          estimate std.error statistic p.value coef.type  
#>   <chr>            <dbl>     <dbl>     <dbl> <lgl>   <chr>      
#> 1 InflMedium       0.566    0.105       5.41 NA      coefficient
#> 2 InflHigh         1.29     0.127      10.1  NA      coefficient
#> 3 TypeApartment   -0.572    0.119      -4.80 NA      coefficient
#> 4 TypeAtrium      -0.366    0.155      -2.36 NA      coefficient
#> 5 TypeTerrace     -1.09     0.151      -7.20 NA      coefficient
#> 6 ContHigh         0.360    0.0955      3.77 NA      coefficient
#> 7 Low|Medium      -0.496    0.125      -3.97 NA      scale      
#> 8 Medium|High      0.691    0.125       5.50 NA      scale

Augment data with information from a(n) polr object

Arguments

See also

Examples