fbvs: Forwards, backwards variable selection, picking variables to...
In matie: Measuring Association and Testing Independence Efficiently

Description Usage Arguments Details Value Note Author(s) References See Also Examples

Attempts to find the best explanatory set of variables to explain a single variable in a data set. Iterates between adding the next best variable to the set and removing the variable (if any) whose exclusion maximizes the overall score.

1	fbvs(dataSet,one,maxv,linear)

`dataSet`	the n x m data frame representing n observations of m variables.
`one`	a string specifying the name of one variable in the dataset, for which the best explanatory set is required. Defaults to the name of the last variable in the dataset.
`maxv`	an integer limiting the maximum number of variables in the explanatory set. Defaults to m-1.
`linear`	a boolean flag which causes fbvs to use a linear model to estimate R^2 instead of matie to estimate A when running the selection algorithm. Defaults to `FALSE`

Variable names are only added to the explanatory set if their inclusion results in an increase in the association measure.

Returns a list containing the following items:

`one`	the name of the one variable that requires the explanatory set
`best`	the best set of explanatory variables
`Rsq`	an estimate for R^2 provided by the best set of explanatory variables

The data set can be of any dimension

Ben Murrell, Dan Murrell & Hugh Murrell.

Discovering general multidimensional associations, http://arxiv.org/abs/1303.1828

ma agram

    # measure association for all pairs in a subrange of the baseball dataset
    data(baseballData)
    fbvs(baseballData,one="Salary")
    fbvs(baseballData,one="Salary",linear=TRUE)

    fbvs(baseballData,one="Salary",maxv=2)
    fbvs(baseballData,one="Salary",maxv=2,linear=TRUE)