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 cmp
tidy(x, conf.int = FALSE, conf.level = 0.95, exponentiate = FALSE, ...)an object class 'cmp' object, obtained from a call to glm.cmp
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.
other arguments passed to or from other methods (currently unused).
A tibble::tibble() with columns:
The name of the regression term.
The estimated value of the regression term.
The standard error of the regression term.
The value of a test statistic to use in a hypothesis that the regression term is non-zero.
The two-sided p-value associated with the observed statistic based on asymptotic normality.
Only for varying dispersion models. Type of coefficient being estimated: 'mu', 'nu'
Lower bound on the confidence interval for the estimate.
Upper bound on the confidence interval for the estimate.
data(attendance)
M.attendance <- glm.cmp(daysabs ~ gender + math + prog, data = attendance)
tidy(M.attendance)
#> # A tibble: 5 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 2.71 0.190 14.3 4.05e-46
#> 2 gendermale -0.215 0.117 -1.83 6.68e- 2
#> 3 math -0.00632 0.00239 -2.65 8.04e- 3
#> 4 progAcademic -0.425 0.170 -2.51 1.21e- 2
#> 5 progVocational -1.25 0.189 -6.62 3.65e-11