Smooth Interpolation of Marks as a Spatial Function
Perform spatial smoothing of numeric values observed at a set of irregular locations, and return the result as a function of spatial location.
## S3 method for class 'ppp': Smoothfun(X, sigma = NULL, \dots, weights = NULL, edge = TRUE, diggle = FALSE)
- Marked point pattern (object of class
- Smoothing bandwidth, or bandwidth selection function,
- Additional arguments passed to
- Optional vector of weights associated with the points of
- Logical arguments controlling the edge correction.
Arguments passed to
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
Smoothfun to interpolate data.
x,y. The function also belongs to the class
"Smoothfun"which has methods for
as.im. It also belongs to the class
"funxy"which has methods for
f <- Smoothfun(longleaf) f f(120, 80) plot(f)