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. 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 plm augment(x, data = model.frame(x), ...)
| x | A |
|---|---|
| data | A base::data.frame or |
| ... | Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
Other plm tidiers:
glance.plm(),
tidy.plm()
A tibble::tibble() with columns:
Fitted or predicted value.
The difference between observed and fitted values.
#> #>#> #> #>#> #> #>#> #> #>data("Produc", package = "plm") zz <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, index = c("state", "year") ) 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#> # A tibble: 4 x 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#> # A tibble: 4 x 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#> # A tibble: 4 x 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#> # A tibble: 816 x 7 #> `log(gsp)` `log(pcap)` `log(pc)` `log(emp)` unemp .fitted .resid #> <pseries> <pseries> <pseries> <pseries> <pseries> <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 #> # … with 806 more rows#> # A tibble: 1 x 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