Methods for the 'broom' generics (from the 'generics' package), registered when 'generics' is loaded.
Usage
# S3 method for class 'fgls'
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)
# S3 method for class 'fgls'
glance(x, ...)
# S3 method for class 'fgls'
augment(
x,
data = NULL,
newdata = NULL,
se_fit = FALSE,
interval = c("none", "confidence"),
conf.level = 0.95,
...
)Arguments
- x
An
"fgls"object.- conf.int
Include confidence intervals.
- conf.level
Confidence level.
- ...
Not used.
- data
Data to augment: by default the model frame. It must have one row per observation used in the fit or, for a fit with
na.action = na.exclude, one row per row of the original data (the excluded rows then getNA).- newdata
Optional new data; fitted values (and, if requested, standard errors and confidence limits) are added, but no residuals.
- se_fit
Add the standard errors of the fitted means (
.se.fit).- interval
"none"or"confidence": add confidence limits for the mean of the response (.lower,.upper) at levelconf.level.
Value
A data frame (a tibble if 'tibble' is installed): one row per
coefficient for tidy(), one row for glance(), one row per observation
for augment() (columns .fitted, optionally .lower, .upper and
.se.fit, then .resid, .innovation and, with a heteroskedasticity
correction, .weight). nonexogeneity.share in glance() is NA when
the share was not computed.
Examples
if (requireNamespace("broom", quietly = TRUE)) {
fit <- serial_fgls(y ~ x, data = simulate_serial(200, seed = 1))
broom::tidy(fit, conf.int = TRUE)
broom::glance(fit)
head(broom::augment(fit))
}
#> # A tibble: 6 × 5
#> y x .fitted .resid .innovation
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2.25 1.06 2.24 0.00406 NA
#> 2 2.08 1.77 2.94 -0.854 -0.858
#> 3 0.878 -1.17 0.0568 0.821 1.52
#> 4 1.48 -0.690 0.524 0.951 0.281
#> 5 3.94 2.83 3.97 -0.0334 -0.810
#> 6 1.42 -0.153 1.05 0.373 0.400