Augment data with information from a(n) ivreg 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 ivreg
augment(x, data = model.frame(x), newdata = NULL, ...)

Arguments

x

An ivreg object created by a call to AER::ivreg().

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.

...

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

This tidier currently only supports ivreg-classed objects outputted by the AER package. The ivreg package also outputs objects of class ivreg, and will be supported in a later release.

Value

A tibble::tibble() with columns:

.fitted: Fitted or predicted value.
.resid: The difference between observed and fitted values.

Examples


# load libraries for models and data
library(AER)
#> Loading required package: car
#> Loading required package: carData
#> 
#> Attaching package: ‘car’
#> The following object is masked from ‘package:purrr’:
#> 
#>     some
#> The following object is masked from ‘package:dplyr’:
#> 
#>     recode

# load data
data("CigarettesSW", package = "AER")

# fit model
ivr <- ivreg(
  log(packs) ~ income | population,
  data = CigarettesSW,
  subset = year == "1995"
)

# summarize model fit with tidiers
tidy(ivr)
#> # A tibble: 2 × 5
#>   term         estimate std.error statistic  p.value
#>   <chr>           <dbl>     <dbl>     <dbl>    <dbl>
#> 1 (Intercept)  4.61e+ 0  4.45e- 2    104.   3.74e-56
#> 2 income      -5.71e-10  2.33e-10     -2.44 1.84e- 2
tidy(ivr, conf.int = TRUE)
#> # A tibble: 2 × 7
#>   term         estimate std.error statistic  p.value conf.low conf.high
#>   <chr>           <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
#> 1 (Intercept)  4.61e+ 0  4.45e- 2    104.   3.74e-56  4.52e+0  4.70e+ 0
#> 2 income      -5.71e-10  2.33e-10     -2.44 1.84e- 2 -1.03e-9 -1.13e-10
tidy(ivr, conf.int = TRUE, instruments = TRUE)
#> # A tibble: 1 × 5
#>   term   num.df den.df statistic  p.value
#>   <chr>   <dbl>  <dbl>     <dbl>    <dbl>
#> 1 income      1     46     3329. 1.46e-44

augment(ivr)
#> # A tibble: 48 × 6
#>    .rownames `log(packs)`    income population .fitted  .resid
#>    <chr>            <dbl>     <dbl>      <dbl>   <dbl>   <dbl>
#>  1 49                4.62  83903280    4262731    4.56  0.0522
#>  2 50                4.71  45995496    2480121    4.59  0.124 
#>  3 51                4.28  88870496    4306908    4.56 -0.285 
#>  4 52                4.04 771470144   31493524    4.17 -0.131 
#>  5 53                4.41  92946544    3738061    4.56 -0.145 
#>  6 54                4.38 104315120    3265293    4.55 -0.177 
#>  7 55                4.82  18237436     718265    4.60  0.223 
#>  8 56                4.53 333525344   14185403    4.42  0.112 
#>  9 57                4.58 159800448    7188538    4.52  0.0591
#> 10 58                4.53  60170928    2840860    4.58 -0.0512
#> # ℹ 38 more rows
augment(ivr, data = CigarettesSW)
#> # A tibble: 96 × 11
#>    state year    cpi population packs    income   tax price  taxs .fitted
#>    <fct> <fct> <dbl>      <dbl> <dbl>     <dbl> <dbl> <dbl> <dbl>   <dbl>
#>  1 AL    1985   1.08    3973000  116.  46014968  32.5 102.   33.3    4.56
#>  2 AR    1985   1.08    2327000  129.  26210736  37   101.   37      4.59
#>  3 AZ    1985   1.08    3184000  105.  43956936  31   109.   36.2    4.56
#>  4 CA    1985   1.08   26444000  100. 447102816  26   108.   32.1    4.17
#>  5 CO    1985   1.08    3209000  113.  49466672  31    94.3  31      4.56
#>  6 CT    1985   1.08    3201000  109.  60063368  42   128.   51.5    4.55
#>  7 DE    1985   1.08     618000  144.   9927301  30   102.   30      4.60
#>  8 FL    1985   1.08   11352000  122. 166919248  37   115.   42.5    4.42
#>  9 GA    1985   1.08    5963000  127.  78364336  28    97.0  28.8    4.52
#> 10 IA    1985   1.08    2830000  114.  37902896  34   102.   37.9    4.58
#> # ℹ 86 more rows
#> # ℹ 1 more variable: .resid <dbl>
augment(ivr, newdata = CigarettesSW)
#> # A tibble: 96 × 10
#>    state year    cpi population packs    income   tax price  taxs .fitted
#>    <fct> <fct> <dbl>      <dbl> <dbl>     <dbl> <dbl> <dbl> <dbl>   <dbl>
#>  1 AL    1985   1.08    3973000  116.  46014968  32.5 102.   33.3    4.59
#>  2 AR    1985   1.08    2327000  129.  26210736  37   101.   37      4.60
#>  3 AZ    1985   1.08    3184000  105.  43956936  31   109.   36.2    4.59
#>  4 CA    1985   1.08   26444000  100. 447102816  26   108.   32.1    4.36
#>  5 CO    1985   1.08    3209000  113.  49466672  31    94.3  31      4.58
#>  6 CT    1985   1.08    3201000  109.  60063368  42   128.   51.5    4.58
#>  7 DE    1985   1.08     618000  144.   9927301  30   102.   30      4.61
#>  8 FL    1985   1.08   11352000  122. 166919248  37   115.   42.5    4.52
#>  9 GA    1985   1.08    5963000  127.  78364336  28    97.0  28.8    4.57
#> 10 IA    1985   1.08    2830000  114.  37902896  34   102.   37.9    4.59
#> # ℹ 86 more rows

glance(ivr)
#> # A tibble: 1 × 8
#>   r.squared adj.r.squared sigma statistic p.value    df df.residual  nobs
#>       <dbl>         <dbl> <dbl>     <dbl>   <dbl> <int>       <int> <int>
#> 1     0.131         0.112 0.229      5.98  0.0184     2          46    48