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

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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