Augment data with information from a(n) ivreg object

Source: R/aer-tidiers.R

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 all columns used to fit the model are present.

Augment will often behavior different depending on whether data or newdata is specified. 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 some 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 ivreg
augment(x, data = model.frame(x), newdata = NULL, ...)

Arguments

x

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

A 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 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. 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 also

augment(), AER::ivreg()

Other ivreg tidiers: glance.ivreg(), tidy.ivreg()

Value

A tibble::tibble() with columns:

.fitted

Fitted or predicted value.

.resid

The difference between fitted and observed values.

Examples


library(AER)
#> Loading required package: car
#> Loading required package: carData
#> 
#> Attaching package: ‘car’
#> The following object is masked from ‘package:dplyr’:
#> 
#>     recode
#> Loading required package: lmtest
#> Loading required package: zoo
#> 
#> Attaching package: ‘zoo’
#> The following objects are masked from ‘package:base’:
#> 
#>     as.Date, as.Date.numeric
#> 
#> Attaching package: ‘lmtest’
#> The following object is masked from ‘package:lfe’:
#> 
#>     waldtest
#> Loading required package: sandwich

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

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

summary(ivr)
#> 
#> Call:
#> ivreg(formula = log(packs) ~ income | population, data = CigarettesSW, 
#>     subset = year == "1995")
#> 
#> Residuals:
#>      Min       1Q   Median       3Q      Max 
#> -0.69305 -0.12941 -0.02257  0.11723  0.58184 
#> 
#> Coefficients:
#>               Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)  4.612e+00  4.454e-02 103.549   <2e-16 ***
#> income      -5.705e-10  2.334e-10  -2.445   0.0184 *  
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Residual standard error: 0.2293 on 46 degrees of freedom
#> Multiple R-Squared: 0.1308,	Adjusted R-squared: 0.1119 
#> Wald test: 5.976 on 1 and 46 DF,  p-value: 0.01839 
#> 

tidy(ivr)
#> # A tibble: 2 x 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 x 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 x 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 x 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
#> # … with 38 more rows
augment(ivr, data = CigarettesSW)
#> # A tibble: 96 x 11
#>    state year    cpi population packs   income   tax price  taxs .fitted  .resid
#>    <fct> <fct> <dbl>      <dbl> <dbl>    <dbl> <dbl> <dbl> <dbl>   <dbl>   <dbl>
#>  1 AL    1985   1.08    3973000  116.   4.60e7  32.5 102.   33.3    4.56  0.0522
#>  2 AR    1985   1.08    2327000  129.   2.62e7  37   101.   37      4.59  0.124 
#>  3 AZ    1985   1.08    3184000  105.   4.40e7  31   109.   36.2    4.56 -0.285 
#>  4 CA    1985   1.08   26444000  100.   4.47e8  26   108.   32.1    4.17 -0.131 
#>  5 CO    1985   1.08    3209000  113.   4.95e7  31    94.3  31      4.56 -0.145 
#>  6 CT    1985   1.08    3201000  109.   6.01e7  42   128.   51.5    4.55 -0.177 
#>  7 DE    1985   1.08     618000  144.   9.93e6  30   102.   30      4.60  0.223 
#>  8 FL    1985   1.08   11352000  122.   1.67e8  37   115.   42.5    4.42  0.112 
#>  9 GA    1985   1.08    5963000  127.   7.84e7  28    97.0  28.8    4.52  0.0591
#> 10 IA    1985   1.08    2830000  114.   3.79e7  34   102.   37.9    4.58 -0.0512
#> # … with 86 more rows
augment(ivr, newdata = CigarettesSW)
#> # A tibble: 96 x 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
#> # … with 86 more rows

glance(ivr)
#> # A tibble: 1 x 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