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.
# NOT RUN {
data(attendance)
M.attendance <- glm.cmp(daysabs~ gender+math+prog, data=attendance)
tidy(M.attendance)
# }
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