Tidy a(n) felm object
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.
## S3 method for class 'felm'
tidy(
x,
conf.int = FALSE,
conf.level = 0.95,
fe = FALSE,
se.type = c("default", "iid", "robust", "cluster"),
...
)x |
A |
conf.int |
Logical indicating whether or not to include a confidence
interval in the tidied output. Defaults to |
conf.level |
The confidence level to use for the confidence interval
if |
fe |
Logical indicating whether or not to include estimates of
fixed effects. Defaults to |
se.type |
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 |
A tibble::tibble() with columns:
conf.high |
Upper bound on the confidence interval for the estimate. |
conf.low |
Lower bound on the confidence interval for the estimate. |
estimate |
The estimated value of the regression term. |
p.value |
The two-sided p-value associated with the observed statistic. |
statistic |
The value of a T-statistic to use in a hypothesis that the regression term is non-zero. |
std.error |
The standard error of the regression term. |
term |
The name of the regression term. |
Other felm tidiers:
augment.felm()
if (requireNamespace("lfe", quietly = TRUE)) {
library(lfe)
# Use built-in "airquality" dataset
head(airquality)
# No FEs; same as lm()
est0 <- felm(Ozone ~ Temp + Wind + Solar.R, airquality)
tidy(est0)
augment(est0)
# Add month fixed effects
est1 <- felm(Ozone ~ Temp + Wind + Solar.R | Month, airquality)
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)
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")
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