Augment data with information from a(n) spatialreg object
Source:R/spdep-tidiers.R
augment.sarlm.RdAugment 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 'sarlm'
augment(x, data = x$X, ...)Arguments
- x
An object returned from
spatialreg::lagsarlm()orspatialreg::errorsarlm().- data
Ignored, but included for internal consistency. See the details below.
- ...
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:
Details
The predict method for sarlm objects assumes that the response is
known. See ?predict.sarlm for more discussion. As a result, since the
original data can be recovered from the fit object, this method
currently does not take in data or newdata arguments.
See also
Other spatialreg tidiers:
glance.sarlm(),
tidy.sarlm()
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(spatialreg)
#> Loading required package: spData
#> To access larger datasets in this package, install the
#> spDataLarge package with: `install.packages('spDataLarge',
#> repos='https://nowosad.github.io/drat/', type='source')`
#> Loading required package: sf
#> Linking to GEOS 3.10.2, GDAL 3.4.1, PROJ 8.2.1; sf_use_s2() is TRUE
library(spdep)
#>
#> Attaching package: ‘spdep’
#> The following objects are masked from ‘package:spatialreg’:
#>
#> get.ClusterOption, get.VerboseOption, get.ZeroPolicyOption,
#> get.coresOption, get.mcOption, set.ClusterOption,
#> set.VerboseOption, set.ZeroPolicyOption, set.coresOption,
#> set.mcOption
# load data
data(oldcol, package = "spdep")
listw <- nb2listw(COL.nb, style = "W")
# fit model
crime_sar <-
lagsarlm(CRIME ~ INC + HOVAL,
data = COL.OLD,
listw = listw,
method = "eigen"
)
# summarize model fit with tidiers
tidy(crime_sar)
#> # A tibble: 4 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 rho 0.431 0.118 3.66 2.50e- 4
#> 2 (Intercept) 45.1 7.18 6.28 3.37e-10
#> 3 INC -1.03 0.305 -3.38 7.23e- 4
#> 4 HOVAL -0.266 0.0885 -3.00 2.66e- 3
tidy(crime_sar, 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 rho 0.431 0.118 3.66 2.50e- 4 0.200 0.662
#> 2 (Intercept) 45.1 7.18 6.28 3.37e-10 31.0 59.1
#> 3 INC -1.03 0.305 -3.38 7.23e- 4 -1.63 -0.434
#> 4 HOVAL -0.266 0.0885 -3.00 2.66e- 3 -0.439 -0.0925
glance(crime_sar)
#> # A tibble: 1 × 6
#> r.squared AIC BIC deviance logLik nobs
#> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 0.652 375. 384. 4679. -182. 49
augment(crime_sar)
#> # A tibble: 49 × 6
#> `(Intercept)` INC HOVAL CRIME .fitted .resid
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 21.2 44.6 18.8 22.6 -3.84
#> 2 1 4.48 33.2 32.4 46.6 -14.2
#> 3 1 11.3 37.1 38.4 41.4 -2.97
#> 4 1 8.44 75 0.178 37.9 -37.7
#> 5 1 19.5 80.5 15.7 14.2 1.54
#> 6 1 16.0 26.4 30.6 34.3 -3.66
#> 7 1 11.3 23.2 50.7 44.7 5.99
#> 8 1 16.0 28.8 26.1 38.4 -12.3
#> 9 1 9.87 18 48.6 51.7 -3.12
#> 10 1 13.6 96.4 34.0 16.3 17.7
#> # ℹ 39 more rows
# fit another model
crime_sem <- errorsarlm(CRIME ~ INC + HOVAL, data = COL.OLD, listw)
# summarize model fit with tidiers
tidy(crime_sem)
#> # A tibble: 4 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 59.9 5.37 11.2 0
#> 2 INC -0.941 0.331 -2.85 0.00441
#> 3 HOVAL -0.302 0.0905 -3.34 0.000836
#> 4 lambda 0.562 0.134 4.20 0.0000271
tidy(crime_sem, 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 (Intercept) 59.9 5.37 11.2 0 49.4 70.4
#> 2 INC -0.941 0.331 -2.85 0.00441 -1.59 -0.293
#> 3 HOVAL -0.302 0.0905 -3.34 0.000836 -0.480 -0.125
#> 4 lambda 0.562 0.134 4.20 0.0000271 0.299 0.824
glance(crime_sem)
#> # A tibble: 1 × 6
#> r.squared AIC BIC deviance logLik nobs
#> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 0.658 377. 386. 4683. -183. 49
augment(crime_sem)
#> # A tibble: 49 × 6
#> `(Intercept)` INC HOVAL CRIME .fitted .resid
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 21.2 44.6 18.8 22.5 -3.70
#> 2 1 4.48 33.2 32.4 44.9 -12.5
#> 3 1 11.3 37.1 38.4 38.2 0.223
#> 4 1 8.44 75 0.178 35.0 -34.8
#> 5 1 19.5 80.5 15.7 13.3 2.45
#> 6 1 16.0 26.4 30.6 35.0 -4.33
#> 7 1 11.3 23.2 50.7 42.3 8.41
#> 8 1 16.0 28.8 26.1 39.4 -13.3
#> 9 1 9.87 18 48.6 49.3 -0.721
#> 10 1 13.6 96.4 34.0 16.6 17.4
#> # ℹ 39 more rows
# fit another model
crime_sac <- sacsarlm(CRIME ~ INC + HOVAL, data = COL.OLD, listw)
# summarize model fit with tidiers
tidy(crime_sac)
#> # A tibble: 5 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 rho 0.368 0.197 1.87 0.0613
#> 2 (Intercept) 47.8 9.90 4.83 0.00000140
#> 3 INC -1.03 0.326 -3.14 0.00167
#> 4 HOVAL -0.282 0.0900 -3.13 0.00176
#> 5 lambda 0.167 0.297 0.562 0.574
tidy(crime_sac, conf.int = TRUE)
#> # A tibble: 5 × 7
#> term estimate std.error statistic p.value conf.low conf.high
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 rho 0.368 0.197 1.87 0.0613 -0.0174 0.754
#> 2 (Intercept) 47.8 9.90 4.83 0.00000140 28.4 67.2
#> 3 INC -1.03 0.326 -3.14 0.00167 -1.67 -0.386
#> 4 HOVAL -0.282 0.0900 -3.13 0.00176 -0.458 -0.105
#> 5 lambda 0.167 0.297 0.562 0.574 -0.415 0.748
glance(crime_sac)
#> # A tibble: 1 × 6
#> r.squared AIC BIC deviance logLik nobs
#> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 0.652 376. 388. 4685. -182. 49
augment(crime_sac)
#> # A tibble: 49 × 6
#> `(Intercept)` INC HOVAL CRIME .fitted .resid
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 21.2 44.6 18.8 22.2 -3.37
#> 2 1 4.48 33.2 32.4 46.4 -14.0
#> 3 1 11.3 37.1 38.4 40.4 -2.00
#> 4 1 8.44 75 0.178 37.5 -37.3
#> 5 1 19.5 80.5 15.7 13.5 2.25
#> 6 1 16.0 26.4 30.6 34.4 -3.74
#> 7 1 11.3 23.2 50.7 44.1 6.60
#> 8 1 16.0 28.8 26.1 39.0 -12.9
#> 9 1 9.87 18 48.6 51.5 -2.93
#> 10 1 13.6 96.4 34.0 15.8 18.2
#> # ℹ 39 more rows