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 negbin 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 |
Other glm.nb tidiers:
glance.negbin()
if (requireNamespace("MASS", quietly = TRUE)) { library(MASS) r <- glm.nb(Days ~ Sex/(Age + Eth*Lrn), data = quine) tidy(r) glance(r) } #> # A tibble: 1 × 8 #> null.deviance df.null logLik AIC BIC deviance df.residual nobs #> <dbl> <int> <logLik> <dbl> <dbl> <dbl> <int> <int> #> 1 235. 145 -531.5125 1093. 1138. 168. 132 146