I get the following error while imputing missing cases with the mice function from the library "mice"
Error in nnet.default(X, Y, w, mask = mask, size = 0, skip = TRUE, softmax = TRUE, :
too many (1104) weights
The problem is generated by the function mice.impute.polr
and mice.impute.polyreg
because of the default maximum number of weights.
I can not solved it by using the command substitute
and neither by copying the functions' code and writing the new functions mice.impute.polr
and mice.impute.polyreg
(because of a function I cannot find call augment
).
I've told that I should go to the source code to modify it.
How can I do it? Are there any other solution?
The neural net function called by mice()
is stopping because the "maximum allowable number of weights" has been exceeded. The MaxNWts
argument to nnet is there to prevent running code that will take a very long time to complete.
If you don't mind waiting then you can increase the MaxNWts
parameter by passing it directly to mice()
, which will be picked up by nnet()
:
mice(data = df_with_nas, MaxNWts = 2000)
Increase the MaxNWts in mice through nnet.MaxNWts argument
mice(data = df_with_nas, nnet.MaxNWts = 2000)
This is described in the documentation of the mice imputation functions, e.g. mice.impute.polr
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