R: Smooth a Signed or Vector-Valued Measure

Smooth.msr {spatstat} | R Documentation

## Smooth a Signed or Vector-Valued Measure

### Description

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

### Usage

     ## S3 method for class '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.

### 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`.

### 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.

### Author(s)

Adrian Baddeley Adrian.Baddeley@curtin.edu.au

### 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

       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))

[Package spatstat version 1.63-3 Index]