I am using the MICE package in R to impute my dataset to deal with missing values. I have missing values in both the response and the predictor variables. I am interested in following Paul T. Von Hippel's (2007) suggestion to impute the data including the response variables but not using the imputed response variables in the glm. I have figured out how to do that in MICE but I would like to also use the data in package mami to run model selection. I can't run the data in mami as it seems to refuse to run if there are any missing data which there are since we are missing data in the response variables. I get the error:
Error in mami(imp, model = "gaussian", outcome = c("j_immerged2"), method = "MA.criterion", : There is still missing data but you specified the data is imputed.
Is there any way around this problem? Is there a different package I can use to run model selection on imputed data with missing data?
P.S. I did run a large number of imputations and impute both the response and predictor variables and for analysis at first. However, reviewers were not particularly fond of the choice to impute the response variables, hence why I am trying to analyze the data as stated above.
citation from above: Von Hippel, P. T. 2007. Regression with missing Ys: an improved strategy for analyzing multiply imputed data. Sociological Methodology, 37: 83-117.