spatstat.core (version 2.3-1)

Smooth.msr: Smooth a Signed or Vector-Valued Measure

Description

Apply kernel smoothing to a signed measure or vector-valued measure.

Usage

# S3 method for msr
Smooth(X, ..., drop=TRUE)

Arguments

X

Object of class "msr" representing a signed measure or vector-valued measure.

…

Arguments passed to density.ppp controlling the smoothing bandwidth and the pixel resolution.

drop

Logical. If TRUE (the default), the result of smoothing a scalar-valued measure is a pixel image. If FALSE, the result of smoothing a scalar-valued measure is a list containing one pixel image.

Value

A pixel image or a list of pixel images. For scalar-valued measures, a pixel image (object of class "im") provided drop=TRUE. For vector-valued measures (or if drop=FALSE), a list of pixel images; the list also belongs to the class "solist" so that it can be printed and plotted.

Details

This function applies kernel smoothing to a signed measure or vector-valued measure X. The Gaussian kernel is used.

The object X would typically have been created by residuals.ppm or msr.

References

Baddeley, A., Turner, R., Moller, J. and Hazelton, M. (2005) Residual analysis for spatial point processes. Journal of the Royal Statistical Society, Series B 67, 617--666.

Baddeley, A., Moller, J. and Pakes, A.G. (2008) Properties of residuals for spatial point processes. Annals of the Institute of Statistical Mathematics 60, 627--649.

See Also

Smooth, msr, plot.msr

Examples

# NOT RUN {
   X <- rpoispp(function(x,y) { exp(3+3*x) })
   fit <- ppm(X, ~x+y)
   rp <- residuals(fit, type="pearson")
   rs <- residuals(fit, type="score")

   plot(Smooth(rp))
   plot(Smooth(rs))
# }