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, ...)
x | An |
---|---|
data | A |
newdata | A |
... | Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
Other ivreg tidiers: glance.ivreg
,
tidy.ivreg
A tibble::tibble()
with columns:
Fitted / predicted value.
Residuals of fitted values. TODO -- document when present. Residuals on which data set?
library(AER)#>#>#> #>#>#> #>#>#>#> #>#>#> #>#> #>#>#> #>#>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#> # 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#> Warning: Exponentiating coefficients, but model did not use a log or logit link function.#> # 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) 101. 4.45e- 2 104. 3.74e-56 92.2 110. #> 2 income 1.000 2.33e-10 -2.44 1.84e- 2 1.000 1.000augment(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#> # 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#> # 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 rowsglance(ivr)#> # A tibble: 1 x 7 #> r.squared adj.r.squared sigma statistic p.value df df.residual #> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int> #> 1 0.131 0.112 0.229 5.98 0.0184 2 46