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I used mice package to impute the missing value as follows:

install.packages("mice")
    library ("mice")
nhanes    
   age  bmi hyp chl
1    1   NA  NA  NA
2    2 22.7   1 187
3    1   NA   1 187
4    3   NA  NA  NA
5    1 20.4   1 113
6    3   NA  NA 184
7    1 22.5   1 118
8    1 30.1   1 187
9    2 22.0   1 238
10   2   NA  NA  NA
11   1   NA  NA  NA
12   2   NA  NA  NA
13   3 21.7   1 206
14   2 28.7   2 204
15   1 29.6   1  NA
16   1   NA  NA  NA
17   3 27.2   2 284
18   2 26.3   2 199
19   1 35.3   1 218
20   3 25.5   2  NA
21   1   NA  NA  NA
22   1 33.2   1 229
23   1 27.5   1 131
24   3 24.9   1  NA
25   2 27.4   1 186 
# imputing the data by using mice
imp=mice(nhanes,**10**) # 10 is mean 10 iteration imputing data (m=10)
fill1=complete(imp,1)  # iteration 1
fill2=complete(imp,2)  # iteration 2
allfill=complete(imp,"long") # all iterations together 

I want to know how to choose imputation (in here I have 10 iterations m=10) as final result to impute the missing data set or by another meaning which imputation is best to impute missing data set ??

And which number of m is feasible and why ? , in here I used 10 iterations (m=10)

imp=mice(df,10) # 10 is mean 10 iteration imputing data 

Also I want some illustrations about analyzing imputations and pooling , how can I benift from the result of analyse that I showed here :

Analyse the result

## Fit models for each imputed dataset
 fit <- with(data = imp, exp = lm(bmi ~ hyp + chl))
 ## Pool results
 poolFit <- pool(fit)
 summary(poolFit)
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  • 1
    $\begingroup$ Are you trying to understand how imputation works (eg, how to combine..., what m is feasible..., etc), OR are you asking for help w/ the R code? Note that the latter is off topic here, but can be on topic on Stack Overflow w/ a reproducible example. $\endgroup$ – gung Dec 27 '15 at 18:16
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    $\begingroup$ yes exactly I want to understand how imputation work not R code . thanks for your advice . but if you have information about it could you help me . $\endgroup$ – zhyan Dec 27 '15 at 19:12
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If you want to choose a single imputed dataset to work with, you should go for single imputation instead. But many authors recommended to use multiple imputation and the estimates will be pooled using Rubin's rule which taken into account between and within variances. Rule of thumb for choosing the number of imputation is one imputation per percent of incomplete data (White et al.,2011)

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  • $\begingroup$ thanks alot for your answer , I used this ways to imputation (mean imputation, hot-dock imputation , knn imputation and mice ) and I want to compare the results of them by using RMSE ,but you know RMSE need orginal dataset and imputed dataset in this situation what I must to do with mice (I mean how to impute my incomplete dataset wth MICE) $\endgroup$ – zhyan Jan 13 '16 at 14:59
  • $\begingroup$ I think this is a different question compared to your previous one. How about posting it as a new question illustrated by R code. $\endgroup$ – pthao Jan 14 '16 at 2:57

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