Hi Josh,
You opened the blackbox up to me. Now I know what is the right way to go.
Thank you so much!
Best,
Fei
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Hi Fei,
On Sat, Nov 26, 2011 at 9:07 AM, Fei wrote:
> Hi Josh,
>
> Thanks for the kind reminder of posting the dataframe on. My dataframe
> contains lots of categorical variables, which seems to be problematic. For
> instance,
>
> dob status edu mrext
> mar
Hi Weidong,
Thank you for the clear explanation. You are right it is not the categorical
variables that are causing the trouble. It might be the relatively small
number of sample that causing the problem given so many variables. I tried
to exclude some variables that are not essential to all the a
Hi Fei,
I wouldn't worry to much about categorical variables for mice. Mice
would use logisitic regression for binary and polytomous logistic
regression for categorical variables with >2 levels. However, you
should not include factors with a lot of levels, saying>30, in
imputation models because i
Hi Josh,
Thanks for the kind reminder of posting the dataframe on. My dataframe
contains lots of categorical variables, which seems to be problematic. For
instance,
dobstatus edu mrext
married highschool yes, full time
Do you know how to specify t
Hi Fei,
On Fri, Nov 25, 2011 at 7:20 PM, Fei wrote:
>> imp<-mice(mydataframe, seed=1)
> When trying the above command, I got the error term:
>
> iter imp variable
> 1 1 medu
> Error in solve.default(xtx + diag(pen)) :
> system is computationally singular: reciprocal condition number =
> 1.1
> imp<-mice(mydataframe, seed=1)
When trying the above command, I got the error term:
iter imp variable
1 1 medu
Error in solve.default(xtx + diag(pen)) :
system is computationally singular: reciprocal condition number =
1.16487e-025/
What does that mean? How can I address this issue? My
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