tidy.svyolr | 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 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 'svyolr'
tidy(x, conf.int = FALSE, conf.level = 0.95, exponentiate = 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 |
exponentiate |
Logical indicating whether or not to exponentiate the
the coefficient estimates. This is typical for logistic and multinomial
regressions, but a bad idea if there is no log or logit link. Defaults
to |
... |
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in
|
The tidy.svyolr()
tidier is a light wrapper around
tidy.polr()
. However, the implementation for p-value calculation
in tidy.polr()
is both computationally intensive and specific to that
model, so the p.values
argument to tidy.svyolr()
is currently ignored,
and will raise a warning when passed.
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. |
tidy, survey::svyolr()
Other ordinal tidiers:
augment.clm()
,
augment.polr()
,
glance.clm()
,
glance.clmm()
,
glance.polr()
,
glance.svyolr()
,
tidy.clm()
,
tidy.clmm()
,
tidy.polr()
library(broom)
library(survey)
data(api)
dclus1 <- svydesign(id = ~dnum, weights = ~pw, data = apiclus1, fpc = ~fpc)
dclus1 <- update(dclus1, mealcat = cut(meals, c(0, 25, 50, 75, 100)))
m <- svyolr(mealcat ~ avg.ed + mobility + stype, design = dclus1)
m
tidy(m, conf.int = TRUE)
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