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 glm
tidy(x, conf.int = FALSE, conf.level = 0.95, exponentiate = FALSE, ...)

## Arguments

x A glm object returned from stats::glm(). 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. 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.

stats::glm()
Other lm tidiers: augment.glm(), augment.lm(), glance.glm(), glance.lm(), glance.summary.lm(), glance.svyglm(), tidy.lm.beta(), tidy.lm(), tidy.mlm(), tidy.summary.lm()