Tidy a fitted model (broom method)

One row per coefficient with its estimate, standard error, z statistic and p-value, for every model class of the package. The two-part classes (hurdle_*, zi_*) have a component column (the broom convention for multi-equation models) and the term prefixed by its component, so that modelsummary lays the table out without a shape argument. A term of a zero-inflation or participation equation that the data do not bound (separation; see zi_cpb()) has no standard error.

Usage

# S3 method for class 'cpb'
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

# S3 method for class 'cpb_fe'
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

# S3 method for class 'gec'
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

# S3 method for class 'count_reg'
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

# S3 method for class 'hurdle_cpb'
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

# S3 method for class 'hurdle_gec'
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

# S3 method for class 'hurdle_count'
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

# S3 method for class 'zi_cpb'
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

# S3 method for class 'zi_gec'
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

# S3 method for class 'zi_count'
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

Arguments

x

A fitted model from this package.

conf.int

If TRUE, add conf.low/conf.high, the intervals of confint() (NA where the fit carries no inference).

conf.level

Confidence level for the interval.

...

Unused.

Value

A data frame with one row per coefficient.

Details

The scale column names what each coefficient multiplies: "log mean" for the mean-parameterized count families ("log untruncated mean" for a zero-truncated fit or a hurdle's intensity), the binary equation's link for a participation or zero-inflation row, and otherwise the family's own parameter ("log rate" for the CPB and GEC, "log lambda" for the rate-parameterized COM-Poisson, "log centrality" for the EWP, and so on; see irr.count). Coefficients on different scales are not comparable across the columns of a table; ame() puts every model's effect on the mean.