Tidy a(n) negbin 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 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.

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

Arguments

See also

Examples