Diagnostic plots for testing the fit of the imputation method to the observed data.
1 2 3 4 5 
x 

y 
currently not used. 
... 
Arguments for other methods, not used. 
m 
The mth imputation. By default is 1. 
vrb 
A chosen variable for the scatter plot. 
vrb.name 
A name of the vrb variable. 
gray.scale 
When set to 
mfrow 
See “par” for details. 
For each variable, observed values are in blue, the imputed values are in red. In the scatterplot the observed and the imputed are plotted versus a variable the users can choose. By default the values are plotted against an index number but it strongly recommended to use a variable containing more information. Fitted lowess lines are also plotted for both observed and imputed data. A small amount of random noise (jittering) is added to the points so that they do not fall on top of each other.
Histograms, scatterplots, and residual plots of the fit of the imputation models. Binned residual plots are for each binary variable.
Masanao Yajima yajima@stat.columbia.edu, YuSung Su suyusung@tsinghua.edu.cn, M.Grazia Pittau grazia@stat.columbia.edu, Andrew Gelman gelman@stat.columbia.edu
YuSung Su, Andrew Gelman, Jennifer Hill, Masanao Yajima. (2011). “Multiple Imputation with Diagnostics (mi) in R: Opening Windows into the Black Box”. Journal of Statistical Software 45(2).
Kobi Abayomi, Andrew Gelman and Marc Levy. (2008). “Diagnostics for multivariate imputations”. Applied Statistics 57, Part 3: 273–291.
Andrew Gelman and Jennifer Hill. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.
mi
,
mi.scatterplot
,
mi.hist
1 2 3 4 5 6  ### NOT RUN
#========================================================
# data(CHAIN)
# imp.CHAIN < mi(CHAIN, n.iter=6, add.noise=FALSE)
# plot(imp.CHAIN)
#========================================================

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