Augment data with ivreg2 model predictions and residuals

Adds .fitted and .resid columns to the model frame (or user-supplied data).

Usage

# S3 method for class 'ivreg2'
augment(x, data = NULL, ...)

Arguments

x

An object of class "ivreg2".

data

A data frame to augment. If NULL (default), uses the stored model frame (x$model) and attaches the stored fitted values and residuals. An error is raised if model = FALSE was used at estimation time and data is not supplied. When data is supplied, .fitted is computed fresh via predict() on the supplied rows, which matches columns by name rather than position; the result is therefore correct for data that is reordered, subsetted, or extended with new rows. Rows with NA in a required predictor receive NA in .fitted. .resid is added only when the response column is present in data (broom convention); it is omitted otherwise. Supplying data for a model fit with partial = raises an error, since predict() cannot score new data after partialling. Because .fitted is a prediction, not an estimation-sample indicator, a row of data that was excluded from estimation only because the response (or an instrument) was NA still receives a real predicted value whenever its regressors are complete; use residuals() or the stored model frame (x$model) to identify which rows were actually used in estimation.

...

Additional arguments (ignored).

Value

A tibble::tibble() with all original data columns plus .fitted, and .resid when the response is available.

See also

ivreg2()

Other broom methods: glance.ivreg2(), tidy.ivreg2()

Examples

data(mroz)
mroz_work <- subset(mroz, inlf == 1)
fit <- ivreg2(lwage ~ exper + expersq | educ | age + kidslt6 + kidsge6,
              data = mroz_work, vcov = "robust")
augment(fit) |> head()
#> # A tibble: 6 × 9
#>    lwage exper expersq  educ   age kidslt6 kidsge6 .fitted  .resid
#>    <dbl> <int>   <int> <int> <int>   <int>   <int>   <dbl>   <dbl>
#> 1 1.21      14     196    12    32       1       0   1.20   0.0107
#> 2 0.329      5      25    12    30       0       2   0.962 -0.634 
#> 3 1.51      15     225    12    35       1       3   1.22   0.297 
#> 4 0.0921     6      36    12    34       0       3   0.995 -0.903 
#> 5 1.52       7      49    14    31       1       2   1.22   0.305 
#> 6 1.56      33    1089    12    54       0       0   1.26   0.299 

# Residuals vs fitted
aug <- augment(fit)
plot(aug$.fitted, aug$.resid, xlab = "Fitted", ylab = "Residuals")
abline(h = 0, lty = 2)