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 plm
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)
A plm
objected returned by plm::plm()
.
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.
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 plm tidiers:
augment.plm()
,
glance.plm()
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("plm", quietly = TRUE)) {
# load libraries for models and data
library(plm)
# load data
data("Produc", package = "plm")
# fit model
zz <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp,
data = Produc, index = c("state", "year")
)
# summarize model fit with tidiers
summary(zz)
tidy(zz)
tidy(zz, conf.int = TRUE)
tidy(zz, conf.int = TRUE, conf.level = 0.9)
augment(zz)
glance(zz)
}
#> # A tibble: 1 × 7
#> r.squared adj.r.squared statistic p.value deviance df.residual nobs
#> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int>
#> 1 0.941 0.937 3065. 0 1.11 764 816