DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining with Rattle Introduction Data Mining with Rattle Data Exploring Data Transforming Data Cluster Analysis Association Analysis Classification and Regression Models Evaluation and Deployment Issues Moving into R Troubleshooting R for the Data Miner R Data Graphics in R Understanding Data Preparing Data Descriptive and Predictive Analytics Issues Evaluating Models Reporting Cluster Analysis Text Mining Text Mining Algorithms Bagging Bayes Classifier Cluster Analysis Conditional Trees Hierarchical Clustering K-Nearest Neighbours Linear Models Neural Networks Support Vector Machines 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 Installing GTK, R, and Rattle Glossary Bibliography Index

Methods

Sometimes you may want to know how a function is implemented. R is also an object oriented function and so what method is called depends on the type of the argument. This can be illustrated with the mean function. Try to determine its implementation:

> mean
function (x, ...)
UseMethod("mean")
<environment: namespace:base>

The UseMethod part indicates that mean is a generic function, possibly with many different implementations. A generic function usually has a default method:

> mean.default
function (x, trim = 0, na.rm = FALSE, ...)
{
    if (!is.numeric(x) && !is.complex(x) && !is.logical(x)) {
        warning("argument is not numeric or logical: returning NA")
        return(as.numeric(NA))
    }
    if (na.rm)
        x <- x[!is.na(x)]
    trim <- trim[1]
    n <- length(x)
[...]
    if (is.integer(x))
        sum(as.numeric(x))/n
    else sum(x)/n
}
<environment: namespace:base>

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