Pool Model Parameters
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.
pool_parameters( x, exponentiate = FALSE, effects = "fixed", component = "conditional", verbose = TRUE, ... )
x |
A list of |
exponentiate |
Logical, indicating whether or not to exponentiate the
the coefficients (and related confidence intervals). This is typical for,
say, logistic regressions, or more generally speaking: for models with log
or logit link. Note: standard errors are also transformed (by
multiplying the standard errors with the exponentiated coefficients), to
mimic behaviour of other software packages, such as Stata. For
|
effects |
Should parameters for fixed effects ( |
component |
Model component for which parameters should be shown. May be
one of |
verbose |
Toggle warnings and messages. |
... |
Currently not used. |
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).
A data frame of indices related to the model's parameters.
Models with multiple components, (for instance, models with zero-inflation,
where predictors appear in the count and zero-inflated 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-inflated model parts would be
combined. Therefore, the component argument defaults to
"conditional" to avoid this.
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.
# 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))
}Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.