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

Missing Values

Missing data can affect modelling, particularly if the data is not randomly missing, but missing because of some underlying systematic reason (e.g., censoring). If data is missing at random then it is more likely that the missing values will have little affect on the modelling.

An excellent reference on dealing with missing data is schafer97:incomplete_data.

Missing values are specially recorded in R as NA. Various functions can be used to check for a missing value (is.na), to remove any entities with missing values (na.omit and to identify those entities that are complete (complete.cases. The apply function also comes in handy here.

> ds <- ds[!apply(is.na(ds),1,all),]    # Remove all rows of all NA's. 
> ds <- na.omit(ds)                     # Remove all rows that have any NA's. 
> ds <- ds[complete.cases(ds),]         # Remove all rows that have any NA's.

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