Structured result container for GLM fits

An S7 class produced by GLM pipelines. Not constructed manually — use define_model() |> prepare_model(GLM) |> conclude() instead.

Inherits from anova_able, so it participates in anova() directly. Downstream packages can use it as a parent in S7::new_class().

Details

Constructor arguments (populated automatically by GLM):

• terms: model terms object.

• df_residual: residual degrees of freedom.

• deviance: scalar deviance.

• dispersion: scalar dispersion parameter.

• family: string naming the error family, e.g. "binomial".

• coefficients: data frame with columns term, estimate, std_error, statistic, p_value.

• fit_summary: data frame with columns family, link, null_deviance, deviance, df_residual, aic, n_obs.

See also

anova_able, GLM

Examples

# Inheriting from class_glm_object in a downstream package:
my_glm = S7::new_class(
    "my_glm",
    parent = class_glm_object
)

# Populating class_glm_object from a fitted glm (as done internally):
fit = glm(am ~ wt + hp, data = mtcars, family = binomial())
s = summary(fit)
fam = fit$family$family

obj = class_glm_object(
    terms = fit$terms,
    df_residual = fit$df.residual,
    deviance = fit$deviance,
    dispersion = if (fam %in% c("binomial", "poisson")) 1 else s$dispersion,
    family = fam,
    coefficients = tibble::tibble(
        term = rownames(coef(s)),
        estimate = coef(s)[, 1],
        std_error = coef(s)[, 2],
        statistic = coef(s)[, 3],
        p_value = coef(s)[, 4]
    ),
    fit_summary = tibble::tibble(
        family = fam,
        link = fit$family$link,
        null_deviance = fit$null.deviance,
        deviance = fit$deviance,
        df_residual = as.integer(fit$df.residual),
        aic = fit$aic,
        n_obs = as.integer(length(fit$residuals))
    )
)