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 plm augment(x, data = model.frame(x), ...)

x | A |
---|---|

data | A |

... | Additional arguments. Not used. Needed to match generic
signature only. |

Other plm tidiers:
`glance.plm()`

,
`tidy.plm()`

A `tibble::tibble()`

with columns:

Fitted or predicted value.

The difference between fitted and observed values.

library(plm)#> #>#>#> #>#>#> #>#>#> #>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-16tidy(zz)#> # 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 -0.00367augment(zz)#> # A tibble: 816 x 7 #> log.gsp. log.pcap. log.pc. log.emp. unemp .fitted .resid #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 10.3 9.62 10.5 6.92 4.7 10.3 -0.0466 #> 2 10.3 9.65 10.5 6.93 5.2 10.3 -0.0306 #> 3 10.4 9.68 10.6 6.98 4.7 10.4 -0.0165 #> 4 10.4 9.71 10.6 7.03 3.9 10.4 -0.00873 #> 5 10.4 9.73 10.6 7.06 5.5 10.5 -0.0271 #> 6 10.4 9.76 10.7 7.05 7.7 10.4 -0.0224 #> 7 10.5 9.78 10.8 7.10 6.8 10.5 -0.0366 #> 8 10.5 9.80 10.8 7.15 7.4 10.6 -0.0300 #> 9 10.6 9.82 10.9 7.20 6.3 10.6 -0.0189 #> 10 10.6 9.85 10.9 7.22 7.1 10.6 -0.0141 #> # … with 806 more rowsglance(zz)#> # 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