I am new to multiple imputation. I followed tutorials that I found online and performed multiple imputations on my own data. Everything went well until the very last step when I need to pool results from different data sets with imputed values. R gave me the following error messages:
pool(rep1_mi)
Error: No tidy method for objects of class qr
In addition: Warning messages:
1: In get.dfcom(object, dfcom) : Infinite sample size assumed.
2: 'tidy.numeric' is deprecated.
See help("Deprecated")
3: 'tidy.numeric' is deprecated.
See help("Deprecated")
4: 'tidy.numeric' is deprecated.
See help("Deprecated")
5: 'tidy.numeric' is deprecated.
See help("Deprecated")
6: 'tidy.numeric' is deprecated.
See help("Deprecated")
7: 'tidy.numeric' is deprecated.
See help("Deprecated")
I didn't find any solution that works. Could anyone please help? Thanks.
This GitHub issue is related to your problem. You can work around it by using the pool.scalar()
function.
Try to run your model directly on the output given by mice
and not on the output given by complete
function
library(psych)
# to create some missingness
bfi[4,1] = NA_character_
bfi[6,2] = NA_character_
bfi[9,1] = NA_character_
bfi[7,2] = NA_character_
bfi[6,1] = NA_character_
# run mice
imput.bfi <- mice(bfi, m = 3)
# when "complete" function is used, "pool" function will not run
bfi.imp.dat=mice::complete(imput.bfi, action="long", inc = TRUE)
# run linear regression
lm.bfi=with(bfi.imp.dat, lm(N1 ~ age))
# pool will not work here
pool(lm.bfi)
# In this case the "pool" function will work properly
# run linear regression
lm.bfi=with(imput.bfi, lm(N1 ~ age))
# pool results
pool(lm.bfi)
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