I used multiple imputation on a data set that had some missing values (I had to do this as the sample size was low so I couldn't just exclude the NAs). I know you can do
*read in dataset*
d <- mice(dat)
fit <- with(data = d, exp = glm(Condition ~ X1 + X2 + X3 + X4))
combine <- pool(fit)
summary(combine)
But are you able to extract predicted values from this model? For example I want the probabilities of individuals being in the control or treatment condition. Normally this would be done like
predict(fit, d, type = 'response')
However I can't seem to make this work. Could I do a prediction for each dataset and average the predicted values? For example:
d <- mice(dat)
a <- complete(d, 1)
fit <- glm(Condition ~ X1 + X2 + X3 + X4, family = 'binomial')
predict(fit, a, type = 'response)
And do this for all datasets then take an average of the predicted values. Or would this logic be flawed and is there any other way to do it?
