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 coefficients (and related confidence intervals). This is typical for logistic regression, or more generally speaking, for models with log or logit links. It is also recommended to use exponentiate = TRUE for models with log-transformed response values. 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

Should all parameters, parameters for the conditional model, for the zero-inflation part of the model, or the dispersion model be returned? Applies to models with zero-inflation and/or dispersion component. component may be one of "conditional", "zi", "zero-inflated", "dispersion" or "all" (default). May be abbreviated.

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).

## Note

Models with multiple components, (for instance, models with zero-inflation, where predictors appear in the count and zero-inflation part) may fail in case of identical names for coefficients in the different model components, since the coefficient table is grouped by coefficient names for pooling. In such cases, coefficients of count and zero-inflation model parts would be combined. Therefore, the component argument defaults to "conditional" to avoid this.

Some model objects do not return standard errors (e.g. objects of class htest). For these models, no pooled confidence intervals nor p-values are returned.

## 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

# 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))
}
#>          term    estimate  std.error statistic        df      p.value
#> 1 (Intercept) 18.14256305 3.54562901  5.116881 11.625350 0.0002813879
#> 2    age40-59 -6.15715380 2.19792337 -2.801351  5.969005 0.0312810653
#> 3    age60-99 -7.72866592 2.45997959 -3.141760  6.642762 0.0174969755
#> 4      hypyes  2.46673562 2.07396774  1.189380  6.147560 0.2781911871
#> 5         chl  0.06028557 0.02078061  2.901050 10.206904 0.0154916183