# How to know which imputation is best for impute my dataset from Multiple imputation by using mice

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)

• 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. Dec 27 '15 at 18:16
• 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 . Dec 27 '15 at 19:12