Tidy a(n) clmm 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 'clmm' 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 |
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. |
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.
Other ordinal tidiers:
augment.clm(),
augment.polr(),
glance.clmm(),
glance.clm(),
glance.polr(),
glance.svyolr(),
tidy.clm(),
tidy.polr(),
tidy.svyolr()
if (requireNamespace("ordinal", quietly = TRUE)) {
library(ordinal)
fit <- clmm(rating ~ temp + contact + (1 | judge), data = wine)
tidy(fit)
tidy(fit, conf.int = TRUE, conf.level = 0.9)
tidy(fit, conf.int = TRUE, exponentiate = TRUE)
glance(fit)
fit2 <- clmm(rating ~ temp + (1 | judge), nominal = ~contact, data = wine)
tidy(fit2)
glance(fit2)
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