pool_parameters {parameters} | R Documentation |
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 |
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 |
effects |
Should parameters for fixed effects ( |
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. |
verbose |
Toggle warnings and messages. |
... |
Arguments passed down to |
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).
Value
A data frame of indices related to the model's parameters.
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
data("nhanes2", package = "mice")
imp <- mice::mice(nhanes2, printFlag = FALSE)
models <- lapply(1:5, function(i) {
lm(bmi ~ age + hyp + chl, data = mice::complete(imp, action = i))
})
pool_parameters(models)
# should be identical to:
m <- with(data = imp, exp = lm(bmi ~ age + hyp + chl))
summary(mice::pool(m))
# For glm, mice used residual df, while `pool_parameters()` uses `Inf`
nhanes2$hyp <- datawizard::slide(as.numeric(nhanes2$hyp))
imp <- mice::mice(nhanes2, printFlag = FALSE)
models <- lapply(1:5, function(i) {
glm(hyp ~ age + chl, family = binomial, data = mice::complete(imp, action = i))
})
m <- with(data = imp, exp = glm(hyp ~ age + chl, family = binomial))
# residual df
summary(mice::pool(m))$df
# df = Inf
pool_parameters(models)$df_error
# use residual df instead
pool_parameters(models, ci_method = "residual")$df_error