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
logistic regression, or more generally speaking, for models with log
or logit links. |

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. |

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

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).

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))
}
#> 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
```