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 class 'plm'
augment(x, data = model.frame(x), ...)
Arguments
- x
A
plm
objected returned byplm::plm()
.- data
A base::data.frame or
tibble::tibble()
containing the original data that was used to produce the objectx
. Defaults tostats::model.frame(x)
so thataugment(my_fit)
returns the augmented original data. Do not pass new data to thedata
argument. Augment will report information such as influence and cooks distance for data passed to thedata
argument. These measures are only defined for the original training data.- ...
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 passconf.lvel = 0.9
, all computation will proceed usingconf.level = 0.95
. Two exceptions here are:
See also
Other plm tidiers:
glance.plm()
,
tidy.plm()
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(plm)
#>
#> Attaching package: ‘plm’
#> The following object is masked from ‘package:mlogit’:
#>
#> has.intercept
#> The following object is masked from ‘package:lfe’:
#>
#> sargan
#> The following objects are masked from ‘package:dplyr’:
#>
#> between, lag, lead
# load data
data("Produc", package = "plm")
# fit model
zz <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp,
data = Produc, index = c("state", "year")
)
# summarize model fit with tidiers
summary(zz)
#> Oneway (individual) effect Within Model
#>
#> Call:
#> plm(formula = log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp,
#> data = Produc, index = c("state", "year"))
#>
#> Balanced Panel: n = 48, T = 17, N = 816
#>
#> Residuals:
#> Min. 1st Qu. Median 3rd Qu. Max.
#> -0.120456 -0.023741 -0.002041 0.018144 0.174718
#>
#> Coefficients:
#> Estimate Std. Error t-value Pr(>|t|)
#> log(pcap) -0.02614965 0.02900158 -0.9017 0.3675
#> log(pc) 0.29200693 0.02511967 11.6246 < 2.2e-16 ***
#> log(emp) 0.76815947 0.03009174 25.5273 < 2.2e-16 ***
#> unemp -0.00529774 0.00098873 -5.3582 1.114e-07 ***
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Total Sum of Squares: 18.941
#> Residual Sum of Squares: 1.1112
#> R-Squared: 0.94134
#> Adj. R-Squared: 0.93742
#> F-statistic: 3064.81 on 4 and 764 DF, p-value: < 2.22e-16
tidy(zz)
#> # A tibble: 4 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 log(pcap) -0.0261 0.0290 -0.902 3.68e- 1
#> 2 log(pc) 0.292 0.0251 11.6 7.08e- 29
#> 3 log(emp) 0.768 0.0301 25.5 2.02e-104
#> 4 unemp -0.00530 0.000989 -5.36 1.11e- 7
tidy(zz, conf.int = TRUE)
#> # A tibble: 4 × 7
#> term estimate std.error statistic p.value conf.low conf.high
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 log(pcap) -0.0261 0.0290 -0.902 3.68e- 1 -0.0830 0.0307
#> 2 log(pc) 0.292 0.0251 11.6 7.08e- 29 0.243 0.341
#> 3 log(emp) 0.768 0.0301 25.5 2.02e-104 0.709 0.827
#> 4 unemp -0.00530 0.000989 -5.36 1.11e- 7 -0.00724 -0.00336
tidy(zz, conf.int = TRUE, conf.level = 0.9)
#> # A tibble: 4 × 7
#> term estimate std.error statistic p.value conf.low conf.high
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 log(pcap) -0.0261 0.0290 -0.902 3.68e- 1 -0.0739 0.0216
#> 2 log(pc) 0.292 0.0251 11.6 7.08e- 29 0.251 0.333
#> 3 log(emp) 0.768 0.0301 25.5 2.02e-104 0.719 0.818
#> 4 unemp -0.00530 0.000989 -5.36 1.11e- 7 -0.00692 -0.00367
augment(zz)
#> # A tibble: 816 × 7
#> `log(gsp)` `log(pcap)` `log(pc)` `log(emp)` unemp .fitted .resid
#> <pseries> <pseries> <pseries> <pseries> <pser> <dbl> <pseries>
#> 1 10.25478 9.617981 10.48553 6.918201 4.7 10.3 -0.046561413
#> 2 10.28790 9.648720 10.52675 6.929419 5.2 10.3 -0.030640422
#> 3 10.35147 9.678618 10.56283 6.977561 4.7 10.4 -0.016454312
#> 4 10.41721 9.705418 10.59873 7.034828 3.9 10.4 -0.008726974
#> 5 10.42671 9.726910 10.64679 7.064588 5.5 10.5 -0.027084312
#> 6 10.42240 9.759401 10.69130 7.052202 7.7 10.4 -0.022368930
#> 7 10.48470 9.783175 10.82420 7.095893 6.8 10.5 -0.036587629
#> 8 10.53111 9.804326 10.84125 7.146142 7.4 10.6 -0.030020604
#> 9 10.59573 9.824430 10.87055 7.197810 6.3 10.6 -0.018942497
#> 10 10.62082 9.845937 10.90643 7.216709 7.1 10.6 -0.014057170
#> # ℹ 806 more rows
glance(zz)
#> # A tibble: 1 × 7
#> r.squared adj.r.squared statistic p.value deviance df.residual nobs
#> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int>
#> 1 0.941 0.937 3065. 0 1.11 764 816