parameters (version 0.16.0)

pool_parameters: Pool Model Parameters

Description

This function "pools" (i.e. combines) model parameters in a similar fashion as mice::pool(). However, this function pools parameters from parameters_model objects, as returned by model_parameters().

Usage

pool_parameters(
  x,
  exponentiate = FALSE,
  effects = "fixed",
  component = "conditional",
  verbose = TRUE,
  ...
)

Arguments

x

A list of parameters_model objects, as returned by model_parameters(), or a list of model-objects that is supported by model_parameters().

exponentiate

Logical, indicating whether or not to exponentiate the the coefficients (and related confidence intervals). This is typical for logistic regression, or more generally speaking, for models with log or logit links. Note: Delta-method standard errors are also computed (by multiplying the standard errors by the transformed coefficients). This is to mimic behaviour of other software packages, such as Stata, but these standard errors poorly estimate uncertainty for the transformed coefficient. The transformed confidence interval more clearly captures this uncertainty. For compare_parameters(), exponentiate = "nongaussian" will only exponentiate coefficients from non-Gaussian families.

effects

Should parameters for fixed effects ("fixed"), random effects ("random"), or both ("all") be returned? Only applies to mixed models. May be abbreviated. If the calculation of random effects parameters takes too long, you may use effects = "fixed".

component

Model component for which parameters should be shown. May be one of "conditional", "precision" (betareg), "scale" (ordinal), "extra" (glmx), "marginal" (mfx), "conditional" or "full" (for MuMIn::model.avg()) or "all".

verbose

Toggle warnings and messages.

...

Currently not used.

Value

A data frame of indices related to the model's parameters.

Details

Averaging of parameters follows Rubin's rules (Rubin, 1987, p. 76). The pooled degrees of freedom is based on the Barnard-Rubin adjustment for small samples (Barnard and Rubin, 1999).

References

Barnard, J. and Rubin, D.B. (1999). Small sample degrees of freedom with multiple imputation. Biometrika, 86, 948-955. Rubin, D.B. (1987). Multiple Imputation for Nonresponse in Surveys. New York: John Wiley and Sons.

Examples

# NOT RUN {
# example for multiple imputed datasets
if (require("mice")) {
  data("nhanes2")
  imp <- mice(nhanes2, printFlag = FALSE)
  models <- lapply(1:5, function(i) {
    lm(bmi ~ age + hyp + chl, data = complete(imp, action = i))
  })
  pool_parameters(models)

  # should be identical to:
  m <- with(data = imp, exp = lm(bmi ~ age + hyp + chl))
  summary(pool(m))
}
# }