wonder if you have some thoughts on running the with() function (and perhaps including the pool() function to get the results?) in glmulti? In other words, how to run glmulti with a data set that is produced by mice()?
publicly available code:
data <- airquality
data[4:10,3] <- rep(NA,7)
data[1:5,4] <- NA
data <- data[-c(5,6)]
the following line will compute the missing data:
tempData <- mice(data,m=5,maxit=50,meth='pmm',seed=500)
and the following 2 lines will run the regression on the mice output and pool the results to establish the final result of interest for the model specified...
modelFit1 <- with(tempData,glm(Temp~ Ozone+Solar.R+Wind))
with glmulti I am trying to establish the "best" model by evaluating combinations of all predictors and interactions in different models and would like to force the variable "Ozone" into all models with the following code:
which will obviously fail once you give it a try... any thoughts on how to identify the best model using glmulti in this fashion that would fit the different combination of predictors with interactions on the mice() output of tempData?