RE: st: Pseudo R2 after "mi estimate:logit"

[Nick Cox <n.j.cox@durham.ac.uk>]

Not at all the question you asked, but my eye fell on 

cond(x==0, mindouble(), log(x3))

This transformation I guess, arises because you feel you should take logs, but there are zeros in your data. But what is mindouble()? In my Stata 

. di mindouble()
-8.99e+307

It is negative and massive! Clearly, I don't know what other values you have in your data, but even if they are of the order of 1e6 or 1e9, that introduced value will still be relatively much larger. 

So, if there really are zeros, it looks as if you intend to introduce massive outliers into your data. 

To be sure, log 0 is indeterminate and logarithms of very small numbers << 1 are large and negative, but it is hard to believe that this way of dealing won't dominate that variable and very likely the whole model results. 

All that said, unless you have elsewhere 

. set type double 

you are generating this variable as a -float-, but a -float- can't hold -mindouble()- and the result would just be missing in those observations.  

Nick 
n.j.cox@durham.ac.uk 

Aggie Chidlow

My do-file is:

mi set mlong
mi query
mi describe
mi misstable sum
generate lnx = cond(x==0, mindouble(), log(x3))
mi register imputed lnx
set seed 29390
mi describe
mi impute mvn lnx = x1 x2 x3 x4 x5 x6 x7,add(30)
mi estimate: logit x1 x2 x3 x4 x5 x6 x7 lnx
mi xeq 0 1 30: logit x1 x2 x3 x4 x5 x6 x7 lnx

For example from "mi xeq 30:logit x1 x2 x3 x4 x5 x6 x7 lnx" I can see
Wald test.However, from "mi estimate: logit x1 x2 x3 x4 x5 x6 x7 lnx"
I can only see F test. So, how can I get Wald test from "mi estimate:
logit"?

Further, how do I know that 30 imputations (M=30) is the adequate
number? I am asking because according to Rubin (1987) only 3-10
imputations may be needed?


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