Pool Predictions or Estimated Marginal Means — pool

This function "pools" (i.e. combines) multiple ggeffects objects, in a similar fashion as mice::pool().

Usage

pool_predictions(x, ...)

Arguments

x: A list of ggeffects objects, as returned by predict_response().
...: Currently not used.

Value

A data frame with pooled predictions.

Details

Averaging of parameters follows Rubin's rules (Rubin, 1987, p. 76). Pooling is applied to the predicted values on the scale of the linear predictor, not on the response scale, in order to have accurate pooled estimates and standard errors. The final pooled predicted values are then transformed to the response scale, using insight::link_inverse().

References

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)
predictions <- lapply(1:5, function(i) {
  m <- lm(bmi ~ age + hyp + chl, data = mice::complete(imp, action = i))
  predict_response(m, "age")
})
pool_predictions(predictions)
#> # Predicted values of bmi
#> 
#> age   | Predicted |       95% CI
#> --------------------------------
#> 20-39 |     29.42 | 12.88, 45.96
#> 40-59 |     23.96 |  4.41, 43.52
#> 60-99 |     22.60 |  1.31, 43.90
#> 
#> Adjusted for:
#> * hyp =     no
#> * chl = 194.64