Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.
A felm
object returned from lfe::felm()
.
Logical indicating whether or not to include a confidence
interval in the tidied output. Defaults to FALSE
.
The confidence level to use for the confidence interval
if conf.int = TRUE
. Must be strictly greater than 0 and less than 1.
Defaults to 0.95, which corresponds to a 95 percent confidence interval.
Logical indicating whether or not to include estimates of
fixed effects. Defaults to FALSE
.
Character indicating the type of standard errors. Defaults to using those of the underlying felm() model object, e.g. clustered errors for models that were provided a cluster specification. Users can override these defaults by specifying an appropriate alternative: "iid" (for homoskedastic errors), "robust" (for Eicker-Huber-White robust errors), or "cluster" (for clustered standard errors; if the model object supports it).
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
. Additionally, if you pass
newdata = my_tibble
to an augment()
method that does not
accept a newdata
argument, it will use the default value for
the data
argument.
Other felm tidiers:
augment.felm()
A tibble::tibble()
with columns:
Upper bound on the confidence interval for the estimate.
Lower bound on the confidence interval for the estimate.
The estimated value of the regression term.
The two-sided p-value associated with the observed statistic.
The value of a T-statistic to use in a hypothesis that the regression term is non-zero.
The standard error of the regression term.
The name of the regression term.
# feel free to ignore the following line—it allows {broom} to supply
# examples without requiring the model-supplying package to be installed.
if (requireNamespace("lfe", quietly = TRUE)) {
# load libraries for models and data
library(lfe)
# use built-in `airquality` dataset
head(airquality)
# no FEs; same as lm()
est0 <- felm(Ozone ~ Temp + Wind + Solar.R, airquality)
# summarize model fit with tidiers
tidy(est0)
augment(est0)
# add month fixed effects
est1 <- felm(Ozone ~ Temp + Wind + Solar.R | Month, airquality)
# summarize model fit with tidiers
tidy(est1)
tidy(est1, fe = TRUE)
augment(est1)
glance(est1)
# 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")
tidy(est1, se.type = "robust")
# 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)
tidy(est2, conf.int = TRUE, se.type = "cluster")
tidy(est2, conf.int = TRUE, se.type = "robust")
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