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 clm
object returned from ordinal::clm()
.
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.
Whether to use "profile"
or "Wald"
confidence
intervals, passed to the type
argument of ordinal::confint.clm()
.
Defaults to "profile"
.
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 FALSE
.
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.
In broom 0.7.0
the coefficient_type
column was renamed to
coef.type
, and the contents were changed as well.
Note that intercept
type coefficients correspond to alpha
parameters, location
type coefficients correspond to beta
parameters, and scale
type coefficients correspond to zeta
parameters.
tidy, ordinal::clm()
, ordinal::confint.clm()
Other ordinal tidiers:
augment.clm()
,
augment.polr()
,
glance.clmm()
,
glance.clm()
,
glance.polr()
,
glance.svyolr()
,
tidy.clmm()
,
tidy.polr()
,
tidy.svyolr()
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("ordinal", quietly = TRUE)) {
# load libraries for models and data
library(ordinal)
# fit model
fit <- clm(rating ~ temp * contact, data = wine)
# summarize model fit with tidiers
tidy(fit)
tidy(fit, conf.int = TRUE, conf.level = 0.9)
tidy(fit, conf.int = TRUE, conf.type = "Wald", exponentiate = TRUE)
glance(fit)
augment(fit, type.predict = "prob")
augment(fit, type.predict = "class")
# ...and again with another model specification
fit2 <- clm(rating ~ temp, nominal = ~contact, data = wine)
tidy(fit2)
glance(fit2)
}
#> # A tibble: 1 × 6
#> edf AIC BIC logLik df.residual nobs
#> <int> <dbl> <dbl> <logLik> <dbl> <dbl>
#> 1 9 190. 211. -86.20855 63 72