spatstat - Fitting a Strauss model with trend determined by kernel density smoother

Dear r-help,

I would like to use the 'ppm' function of the 'spatstat' package to
fit a Strauss inhibition model.  I understand that I can specify a
parametric model for the "background" trend, but how would I specify a
trend which is estimated using a Kernel density smoother?

In particular, I would like to use the 'kde' function of the 'ks'
package to estimate the "background" intensity and then use this as
the trend for a Strauss inhibition process.

Questions about a specific contributed package should usually be  
directed to the maintainers of
that package rather than to r-help.

To attempt to answer your question:

	You need to convert your estimate of the background trend to an  
***image***; see the
	function im() in the spatstat package.

	Or instead of using kde, you could use the ppp method for density()  
which is provided in
	spatstat; this methop returns an image.  See the help for density.ppp 
().

	Now suppose that your point pattern is ``X'' and your estimate of  
the trend is ``bgim''.

	You can then fit the model you want via

		fit <- ppm(X,~bgim,inter=Strauss(42),covariates=list(bgim=bgim))

	Note that you have to specify the interaction radius for the Strauss  
model (I have specified this
	radius to be 42 in the forgoing example); this radius is an  
``irregular'' parameter --- i.e. it does
	not appear in exponential family form --- and hence is not estimated  
by ppm(), at least not
	directly.

				cheers,

					Rolf Turner

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