DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining Data Mining Data Mining with Rattle Introduction Data Transform Explore A Model Building Framework Unsupervised Modelling Two Class Models Multi Class Models Regression Models Text Mining Evaluation and Deployment Moving into R Troubleshooting R for the Data Miner R Data Graphics in R Understanding Data Preparing Data Building Models Evaluating Models Algorithms Apriori Bagging Bayes Classifier Boosting Cluster Analysis Conditional Trees Hierarchical Clustering K-Means K-Nearest Neighbours Linear Models Logistic Regression Neural Networks Support Vector Machines Text Mining Open Products AlphaMiner Borgelt Data Mining Suite KNime R Rattle Weka Closed Products C4.5 Clementine Equbits Foresight GhostMiner InductionEngine ODM Enterprise Miner Statistica Data Miner TreeNet Virtual Predict Appendicies Glossary Bibliography Index

Imputation

Multiple imputation (MI) is a general purpose method for handling of missing data. The basic idea is: Impute missing values using an appropriate model that incorporates random variation; Do this $m$ times (often 3-5 times) to obtain $m$ datasets, all with no missing values; Do the intended analysis on each of these datasets; Gert the average values of the parameter estimates across the $m$ samples to have a single point estimate; Calculate standard errors by firstly averaging the squared standard errors of the $m$ estimates and calculating the variance of the $m$ parameter estimates across samples, and then combine these in some way.

There are a number of R packages for imputation.

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