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Summary method for beezdemand_nlme

Source: R/mixed-methods.R
summary.beezdemand_nlme.Rd

Returns a structured summary object containing model coefficients, fit statistics, and random effects information.

Usage

# S3 method for class 'beezdemand_nlme'
summary(
  object,
  report_space = c("natural", "log10"),
  df_method = c("containment", "between"),
  ...
)

Arguments

object

A beezdemand_nlme object

report_space

Character. Reporting space for core parameters. One of "natural" or "log10" (match.arg default "natural"). estimate/std.error follow this scale; statistic/p.value are always on the estimation scale (nlme's native containment-t test, which is transformation-invariant).

df_method

Character. Degrees of freedom used for the fixed-effect t tests. "containment" (default) reports nlme's own containment df unchanged. nlme's containment rule assigns the observation-level residual degrees of freedom to every fixed effect, including between-subject terms (a group factor), for which the effective sample size is the number of subjects; the default df and p-values for such terms are therefore anticonservative. "between" replaces the df of each between-subject term (a coefficient whose design column is constant within every subject, excluding the parameter intercepts) with n_subjects - rank(X_between), where X_between is that parameter's subject-level design (intercept plus its between-subject columns), and recomputes the p-value from the unchanged t statistic. Intercepts and within-subject terms keep containment df in both modes (Pinheiro & Bates, 2000, section 2.4.2). Estimates and standard errors never change. Satterthwaite / Kenward-Roger df are not available for nlme::nlme(). The TMB backend reports an asymptotic z test instead.

...

Additional arguments (passed to summary.nlme)

Value

A summary.beezdemand_nlme object (inherits from beezdemand_summary) with fields including:

  • call: The original function call

  • model_class: "beezdemand_nlme"

  • backend: "nlme"

  • equation_form: The equation form used ("zben" or "simplified")

  • coefficients: Tibble of fixed effects with std.error, statistic, p.value and df (the degrees of freedom the p-value uses; see df_method)

  • df_method: The df_method in effect

  • random_effects: VarCorr output for random effects

  • logLik, AIC, BIC: Model fit statistics

References

Pinheiro, J. C., & Bates, D. M. (2000). Mixed-Effects Models in S and S-PLUS. Springer. Section 2.4.2.