[R] 2 D non-parametric density estimation

[Thomas W Blackwell]

John  -

My recollection is that Adrian Raftery's contributed package 'mclust'
does kernel density estimation as well.  Not sure whether it does what
you need.  Take a look at it on CRAN.  Ah..I see that the description
which shows up on Jon Baron's search page is not encouraging.  Give it
a try, anyway.  That description does not do it justice.

-  tom blackwell  -  u michigan medical school  -  ann arbor  -

On Wed, 22 Oct 2003, John Fieberg wrote:

> I have spatial data in 2 dimensions - say (x,y).  The correlation
> between x and y is fairly substantial.  My goal is to use a
> non-parametric approach to estimate the multivariate density describing
> the spatial locations.  Ultimately, I would like to use this estimated
> density to determine the area associated with a 95% probability contour
> for the data.
>
> Given the strong correlation between x and y, I have not been real
> happy w/ the results obtained using kernel density estimators with
> separate smoothing parameters for the x and y directions - e.g., bkde2D
> (KernSmooth library), sm (sm library), kde2d (MASS library).   It seems
> to me that a better alternative would be to transform the data to have
> ~0 correlation, estimate the density, then transform back to the
> original scale.  Does this seem reasonable for this sort of problem?
> Has anyone written code in R to do this sort of thing?
>
> I also attempted to explore local likelihood fitting (using locfit
> library).  I liked the look of the estimated densities, but found it
> difficult to obtain predictions at an arbitrary set of grid points (as
> needed to determine a 95% probability contour).  Does anyone have
> examples using locfit w/ the "ev" option or predict.locfit in order to
> obtain local likelihood density estimates at an arbitrary set of grid
> points?
>
> Any suggestions would be greatly appreciated!
>
> John
>
> John Fieberg, Ph.D.
> Wildlife Biometrician, MN DNR
> 5463-C W. Broadway
> Forest Lake, MN 55434
> Phone: (651) 296-2704
>
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