Augment data with information from a(n) felm 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 class 'felm'
augment(x, data = model.frame(x), ...)

Arguments

x

A felm object returned from lfe::felm().

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(lfe)
#> Loading required package: Matrix
#> 
#> Attaching package: ‘Matrix’
#> The following objects are masked from ‘package:tidyr’:
#> 
#>     expand, pack, unpack
#> 
#> Attaching package: ‘lfe’
#> The following object is masked from ‘package:lmtest’:
#> 
#>     waldtest

# use built-in `airquality` dataset
head(airquality)
#>   Ozone Solar.R Wind Temp Month Day
#> 1    41     190  7.4   67     5   1
#> 2    36     118  8.0   72     5   2
#> 3    12     149 12.6   74     5   3
#> 4    18     313 11.5   62     5   4
#> 5    NA      NA 14.3   56     5   5
#> 6    28      NA 14.9   66     5   6

# no FEs; same as lm()
est0 <- felm(Ozone ~ Temp + Wind + Solar.R, airquality)

# summarize model fit with tidiers
tidy(est0)
#> # A tibble: 4 × 5
#>   term        estimate std.error statistic       p.value
#>   <chr>          <dbl>     <dbl>     <dbl>         <dbl>
#> 1 (Intercept) -64.3      23.1        -2.79 0.00623      
#> 2 Temp          1.65      0.254       6.52 0.00000000242
#> 3 Wind         -3.33      0.654      -5.09 0.00000152   
#> 4 Solar.R       0.0598    0.0232      2.58 0.0112       
augment(est0)
#> # A tibble: 111 × 7
#>    .rownames Ozone  Temp  Wind Solar.R .fitted  .resid
#>    <chr>     <int> <int> <dbl>   <int>   <dbl>   <dbl>
#>  1 1            41    67   7.4     190   33.0    7.95 
#>  2 2            36    72   8       118   35.0    1.00 
#>  3 3            12    74  12.6     149   24.8  -12.8  
#>  4 4            18    62  11.5     313   18.5   -0.475
#>  5 7            23    65   8.6     299   32.3   -9.26 
#>  6 8            19    59  13.8      99   -6.95  25.9  
#>  7 9             8    61  20.1      19  -29.4   37.4  
#>  8 12           16    69   9.7     256   32.6  -16.6  
#>  9 13           11    66   9.2     290   31.4  -20.4  
#> 10 14           14    68  10.9     274   28.1  -14.1  
#> # ℹ 101 more rows

# add month fixed effects
est1 <- felm(Ozone ~ Temp + Wind + Solar.R | Month, airquality)

# summarize model fit with tidiers
tidy(est1)
#> # A tibble: 3 × 5
#>   term    estimate std.error statistic     p.value
#>   <chr>      <dbl>     <dbl>     <dbl>       <dbl>
#> 1 Temp      1.88      0.341       5.50 0.000000274
#> 2 Wind     -3.11      0.660      -4.71 0.00000778 
#> 3 Solar.R   0.0522    0.0237      2.21 0.0296     
tidy(est1, fe = TRUE)
#> # A tibble: 8 × 7
#>   term    estimate std.error statistic     p.value     N  comp
#>   <chr>      <dbl>     <dbl>     <dbl>       <dbl> <int> <dbl>
#> 1 Temp      1.88      0.341       5.50 0.000000274    NA    NA
#> 2 Wind     -3.11      0.660      -4.71 0.00000778     NA    NA
#> 3 Solar.R   0.0522    0.0237      2.21 0.0296         NA    NA
#> 4 Month.5 -74.2       4.23      -17.5  2.00           24     1
#> 5 Month.6 -89.0       6.91      -12.9  2.00            9     1
#> 6 Month.7 -83.0       4.06      -20.4  2              26     1
#> 7 Month.8 -78.4       4.32      -18.2  2.00           23     1
#> 8 Month.9 -90.2       3.85      -23.4  2              29     1
augment(est1)
#> # A tibble: 111 × 8
#>    .rownames Ozone  Temp  Wind Solar.R Month .fitted .resid
#>    <chr>     <int> <int> <dbl>   <int> <int>   <dbl>  <dbl>
#>  1 1            41    67   7.4     190     5   38.3    2.69
#>  2 2            36    72   8       118     5   42.1   -6.07
#>  3 3            12    74  12.6     149     5   33.1  -21.1 
#>  4 4            18    62  11.5     313     5   22.6   -4.62
#>  5 7            23    65   8.6     299     5   36.5  -13.5 
#>  6 8            19    59  13.8      99     5   -1.33  20.3 
#>  7 9             8    61  20.1      19     5  -21.3   29.3 
#>  8 12           16    69   9.7     256     5   38.4  -22.4 
#>  9 13           11    66   9.2     290     5   36.1  -25.1 
#> 10 14           14    68  10.9     274     5   33.7  -19.7 
#> # ℹ 101 more rows
glance(est1)
#> # A tibble: 1 × 8
#>   r.squared adj.r.squared sigma statistic  p.value    df df.residual  nobs
#>       <dbl>         <dbl> <dbl>     <dbl>    <dbl> <dbl>       <dbl> <int>
#> 1     0.637         0.612  20.7      25.8 4.57e-20   103         103   111

# the "se.type" argument can be used to switch out different standard errors
# types on the fly. In turn, this can be useful exploring the effect of
# different error structures on model inference.
tidy(est1, se.type = "iid")
#> # A tibble: 3 × 5
#>   term    estimate std.error statistic     p.value
#>   <chr>      <dbl>     <dbl>     <dbl>       <dbl>
#> 1 Temp      1.88      0.341       5.50 0.000000274
#> 2 Wind     -3.11      0.660      -4.71 0.00000778 
#> 3 Solar.R   0.0522    0.0237      2.21 0.0296     
tidy(est1, se.type = "robust")
#> # A tibble: 3 × 5
#>   term    estimate std.error statistic     p.value
#>   <chr>      <dbl>     <dbl>     <dbl>       <dbl>
#> 1 Temp      1.88      0.344       5.45 0.000000344
#> 2 Wind     -3.11      0.903      -3.44 0.000834   
#> 3 Solar.R   0.0522    0.0226      2.31 0.0227     

# add clustered SEs (also by month)
est2 <- felm(Ozone ~ Temp + Wind + Solar.R | Month | 0 | Month, airquality)

# summarize model fit with tidiers
tidy(est2, conf.int = TRUE)
#> # A tibble: 3 × 7
#>   term    estimate std.error statistic  p.value conf.low conf.high
#>   <chr>      <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
#> 1 Temp      1.88      0.182      10.3  0.000497   1.37       2.38 
#> 2 Wind     -3.11      1.31       -2.38 0.0760    -6.74       0.518
#> 3 Solar.R   0.0522    0.0408      1.28 0.270     -0.0611     0.166
tidy(est2, conf.int = TRUE, se.type = "cluster")
#> # A tibble: 3 × 7
#>   term    estimate std.error statistic  p.value conf.low conf.high
#>   <chr>      <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
#> 1 Temp      1.88      0.182      10.3  0.000497   1.37       2.38 
#> 2 Wind     -3.11      1.31       -2.38 0.0760    -6.74       0.518
#> 3 Solar.R   0.0522    0.0408      1.28 0.270     -0.0611     0.166
tidy(est2, conf.int = TRUE, se.type = "robust")
#> # A tibble: 3 × 7
#>   term    estimate std.error statistic p.value conf.low conf.high
#>   <chr>      <dbl>     <dbl>     <dbl>   <dbl>    <dbl>     <dbl>
#> 1 Temp      1.88      0.344       5.45 0.00550   0.920      2.83 
#> 2 Wind     -3.11      0.903      -3.44 0.0262   -5.62      -0.602
#> 3 Solar.R   0.0522    0.0226      2.31 0.0817   -0.0104     0.115
tidy(est2, conf.int = TRUE, se.type = "iid")
#> # A tibble: 3 × 7
#>   term    estimate std.error statistic p.value conf.low conf.high
#>   <chr>      <dbl>     <dbl>     <dbl>   <dbl>    <dbl>     <dbl>
#> 1 Temp      1.88      0.341       5.50 0.00532   0.929      2.82 
#> 2 Wind     -3.11      0.660      -4.71 0.00924  -4.94      -1.28 
#> 3 Solar.R   0.0522    0.0237      2.21 0.0920   -0.0135     0.118

Augment data with information from a(n) felm object

Usage

Arguments

See also

Value

Examples