I am somewhat new to mice but I am trying my best. So I successfully imputed my data and created five datasets.
For the sake of simplicity, let's just use the nhanes dataset that comes with mice
imp <- mice(nhanes, seed = 23109)
In my paper, I need to show basic descriptives (so for example: mean, standard deviation, skewness, kurtosis etc.). I understand that I can pool the parameters and results of my analysis with Rubin's rules using the following syntax:
fit <- with(imp, lm(chl ~ age + bmi)) print(pool(fit)) Call: pool(object = fit) Pooled coefficients: (Intercept) age bmi -34.158914 34.330666 6.212025 Fraction of information about the coefficients missing due to nonresponse: (Intercept) age bmi 0.5747265 0.7501284 0.4795427
But how would I go about this to report basic descriptives? Is there a way to combine these and a function in the mice package that does this for me, or do I have to do this manually? So for example I tried:
mean.fit <- with(data=imp, expr=mean(bmi)) pool(mean.fit)
and , I get the warning:
Error in pool(mean.fit) : Object has no coef() method.
which obviously means that is not how it should be done.
A different (and easier) possibility would be to pick just one of the imputed datasets to showcase (they are all very similar anyway. However, it feels like this wouldn't be a recommended thing to do because the whole upside of using multiple imputation should be that I am not pretending that the imputed values are the real values but that they come with a certain uncertainty.
If anyone could help me, I'd be very happy. Thanks.