Perform spatial smoothing of numeric values observed at a set of irregular locations, and return the result as a function of spatial location.

`Smoothfun(X, ...)`# S3 method for ppp
Smoothfun(X, sigma = NULL, ...,
weights = NULL, edge = TRUE, diggle = FALSE)

A `function`

with arguments `x,y`

.
The function also belongs to the class `"Smoothfun"`

which has
methods for `print`

and `as.im`

.
It also belongs to the class `"funxy"`

which has methods
for `plot`

, `contour`

and `persp`

.

- X
Marked point pattern (object of class

`"ppp"`

).- sigma
Smoothing bandwidth, or bandwidth selection function, passed to

`Smooth.ppp`

.- ...
Additional arguments passed to

`Smooth.ppp`

.- weights
Optional vector of weights associated with the points of

`X`

.- edge,diggle
Logical arguments controlling the edge correction. Arguments passed to

`Smooth.ppp`

.

Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Rolf Turner r.turner@auckland.ac.nz and Ege Rubak rubak@math.aau.dk.

The commands `Smoothfun`

and `Smooth`

both perform kernel-smoothed spatial interpolation
of numeric values observed at irregular spatial locations.
The difference is that `Smooth`

returns a pixel image,
containing the interpolated values at a grid of locations, while
`Smoothfun`

returns a `function(x,y)`

which can be used
to compute the interpolated value at *any* spatial location.
For purposes such as model-fitting it is more accurate to
use `Smoothfun`

to interpolate data.

`Smooth`

```
f <- Smoothfun(longleaf)
f
f(120, 80)
plot(f)
```

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