kde2dForSubset: Compute the density based distance quantity using a 2D Kernel...
In UPMASK: Unsupervised Photometric Membership Assignment in Stellar Clusters

Description Usage Arguments Value Author(s) References Examples

kde2dForSubset will compute the 2D Kernel Density Estimation for the requested subset of data and will return the quantiy (max(d)-mean(d))/sd(d) if the option returnDistance is set to TRUE.

1 2	kde2dForSubset(df, setw=1, n=50, showStats=TRUE, printPlots=TRUE, returnDistance=FALSE, positionDataIndexes=c(1,2))

`df`	a data frame to use
`setw`	an integer with the class of the stars to perform the analysis
`n`	the number of points in the regular grid of the density estimation
`showStats`	a boolean indicating if the user wants to see output statistics
`printPlots`	a boolean indicating if the user wants to see plots
`returnDistance`	a boolean indicating if the distance between the max and the mean in units of standard deviations should be returned
`positionDataIndexes`	an array of integers indicating the columns of the file containing the spatial position measurements

A double representing the density based distance quantity.

Alberto Krone-Martins, Andre Moitinho

Krone-Martins, A. & Moitinho, A., A&A, v.561, p.A57, 2014

# Create a simple data set with the values and errors
toyDataDF <- data.frame(x=runif(50, 0, 10), y=runif(50, 0, 10), resMclust.class=rep(1, 50))

# Run the KDE 2D analysis for the required subset
disV <- kde2dForSubset(toyDataDF, showStats=FALSE, printPlots=FALSE, returnDistance=TRUE)

# Clean the environment
rm(list=c("toyDataDF", "disV"))