pool_predictions | R Documentation |
This function "pools" (i.e. combines) multiple estimate_means
objects, in
a similar fashion as mice::pool()
.
pool_predictions(x, transform = NULL, ...)
pool_slopes(x, transform = NULL, ...)
x |
A list of |
transform |
A function applied to predictions and confidence intervals
to (back-) transform results, which can be useful in case the regression
model has a transformed response variable (e.g., |
... |
Currently not used. |
Averaging of parameters follows Rubin's rules (Rubin, 1987, p. 76).
Pooling is applied to the predicted values and based on the standard errors
as they are calculated in the estimate_means
or estimate_predicted
objects provided in x
. For objects of class estimate_means
, the predicted
values are on the response scale by default, and standard errors are
calculated using the delta method. Then, pooling estimates and calculating
standard errors for the pooled estimates based ob Rubin's rule is carried
out. There is no back-transformation to the link-scale of predicted values
before applying Rubin's rule.
A data frame with pooled predictions.
Rubin, D.B. (1987). Multiple Imputation for Nonresponse in Surveys. New York: John Wiley and Sons.
# example for multiple imputed datasets
data("nhanes2", package = "mice")
imp <- mice::mice(nhanes2, printFlag = FALSE)
# estimated marginal means
predictions <- lapply(1:5, function(i) {
m <- lm(bmi ~ age + hyp + chl, data = mice::complete(imp, action = i))
estimate_means(m, "age")
})
pool_predictions(predictions)
# estimated slopes (marginal effects)
slopes <- lapply(1:5, function(i) {
m <- lm(bmi ~ age + hyp + chl, data = mice::complete(imp, action = i))
estimate_slopes(m, "chl")
})
pool_slopes(slopes)
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