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

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

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.

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.

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`

.

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.

# 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)) # }