tidy.felm {broom} | R Documentation |
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 cross 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, ...)
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 |
... |
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
A tibble::tibble()
with one row for each term in the
regression. The tibble has columns:
term |
The name of the regression term. |
estimate |
The estimated value of the regression term. |
std.error |
The standard error of the regression term. |
statistic |
The value of a statistic, almost always a T-statistic, to use in a hypothesis that the regression term is non-zero. |
p.value |
The two-sided p-value associated with the observed statistic. |
conf.low |
The low end of a confidence interval for the regression
term. Included only if |
conf.high |
The high end of a confidence interval for the regression
term. Included only if |
If fe = TRUE
, also includes rows for for fixed effects estimates.
Other felm tidiers:
augment.felm()
if (require("lfe", quietly = TRUE)) { library(lfe) N=1e2 DT <- data.frame( id = sample(5, N, TRUE), v1 = sample(5, N, TRUE), v2 = sample(1e6, N, TRUE), v3 = sample(round(runif(100,max=100),4), N, TRUE), v4 = sample(round(runif(100,max=100),4), N, TRUE) ) result_felm <- felm(v2~v3, DT) tidy(result_felm) augment(result_felm) result_felm <- felm(v2~v3|id+v1, DT) tidy(result_felm, fe = TRUE) augment(result_felm) v1<-DT$v1 v2 <- DT$v2 v3 <- DT$v3 id <- DT$id result_felm <- felm(v2~v3|id+v1) tidy(result_felm) augment(result_felm) glance(result_felm) }