Hi All,

I'm trying to impute missing data for a multivariate analysis experiment using MICE package. All steps work fine except the last step where I need to pool results of all predictive models as it requires the combined result in the form of a mira object. Any suggestions on conversion to mira objets and pooling is much appreciated!

the code I tried is as below,

<h6>#</h6>

path <- "../Data/Tutorial"

data <- iris

library(missForest)

iris.mis <- prodNA(iris, noNA = 0.1)

iris.mis <- subset(iris.mis, select = -c(Species))

library(mice)

md.pattern(iris.mis)

library(VIM)

imputed_Data <- mice(iris.mis, m=5, maxit = 50, method = 'pmm', seed = 500)

summary(imputed_Data)

mice(data = iris.mis, m = 5, method = "pmm", maxit = 50, seed = 500)

imputed_Data$imp$Sepal.Width

fit <- with(data = iris.mis, exp = lm(Sepal.Width ~ Sepal.Length + Petal.Width)) # builds predictive model

combine <- pool(fit) #### this command doesn't work as it requires "fit" to be a mira object before it could be pooled.

<h6>#</h6>

Thank you!