pd: Plot density (after an 'ma' calculation).
In matie: Measuring Association and Testing Independence Efficiently

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

Optimal kernel widths output by ma are employed to recompute the weighted joint distribution for two variables in a data set, and a contour plot for this distribution is drawn.

1	pd(d,iv=1,jv=2)

`d`	an n x m data frame with m > 1.
`iv`	the column index of the independent variable
`jv`	the column index of the dependent variable

A data set of two variables is extracted from the user's data set and a full distribution is calculated using weighted marginal and joint likelihoods. The optimal kernel sizes and weighting are first computed via a call to ma.

An n x n distribution of weighted likelihoods is returned.

The data set must contain at least 2 columns.

Ben Murrell, Dan Murrell & Hugh Murrell.

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

ma

    f <- function(x,name="Sinusoidal",def="y = 1 + sin(x)"){
      return(1 + sin(x))
    }
    d <- sbd(f,min=-2*pi,max=2*pi,n=1000,Rsq=0.9)
    ma(d)$A
    dist <- pd(d)