Augment data with information from a(n) plm object

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

Arguments

x

A plm objected returned by plm::plm().

data

A base::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.

...

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. Two exceptions here are:

tidy() methods will warn when supplied an exponentiate argument if it will be ignored.
augment() methods will warn when supplied a newdata argument if it will be ignored.

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

Augment data with information from a(n) plm object

Usage

Arguments

See also

Value

Examples