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, ...)

Arguments

x

A glm.nb object returned by MASS::glm.nb().

conf.int

Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to FALSE.

conf.level

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.

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 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.

See also

MASS::glm.nb()

Other glm.nb tidiers: glance.negbin()

Examples

library(MASS) r <- glm.nb(Days ~ Sex/(Age + Eth*Lrn), data = quine) tidy(r)
#> # A tibble: 14 x 5 #> term estimate std.error statistic p.value #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 (Intercept) 3.02 0.297 10.2 2.89e-24 #> 2 SexM -0.475 0.396 -1.20 2.29e- 1 #> 3 SexF:AgeF1 -0.709 0.323 -2.19 2.83e- 2 #> 4 SexM:AgeF1 -0.724 0.330 -2.19 2.85e- 2 #> 5 SexF:AgeF2 -0.615 0.371 -1.66 9.78e- 2 #> 6 SexM:AgeF2 0.628 0.274 2.30 2.17e- 2 #> 7 SexF:AgeF3 -0.342 0.327 -1.05 2.95e- 1 #> 8 SexM:AgeF3 1.15 0.314 3.67 2.46e- 4 #> 9 SexF:EthN -0.0731 0.265 -0.276 7.83e- 1 #> 10 SexM:EthN -0.679 0.256 -2.65 8.07e- 3 #> 11 SexF:LrnSL 0.944 0.322 2.93 3.43e- 3 #> 12 SexM:LrnSL 0.239 0.336 0.712 4.76e- 1 #> 13 SexF:EthN:LrnSL -1.36 0.377 -3.60 3.16e- 4 #> 14 SexM:EthN:LrnSL 0.761 0.441 1.73 8.45e- 2
glance(r)
#> # A tibble: 1 x 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