| image.smooth {fields} | R Documentation |
Takes an image matrix and applies a kernel smoother to it. Missing values are handled using the Nadaraya/Watson normalization of the kernel.
image.smooth(x, wght = NULL, dx = 1, dy = 1, Nwidth = nrow(Y), Mwidth =
ncol(Y),
kernel.function = double.exp,
theta = 1, grid = NULL, tol = 1e-08, xwidth = NULL, ywidth = NULL,
weights = NULL,...)
x |
A matrix image. Missing values can be indicated by NAs. |
wght |
FFT of smoothing kernel. If this is NULL the default is to compute this object. |
grid |
A list with x and y components. Each are equally spaced and define the rectangular. ( see grid.list) |
dx |
Grid spacing in x direction |
dy |
Grid spacing in x direction |
Nwidth |
Half the width of the kernel in the y direction in numbers of grid points. Default is half the number of columns of Y |
Mwidth |
Half the width of the kernel in the x direction in numbers of grid points. Default is half the number of columns of Y |
kernel.function |
An R function that takes as its argument the squared distance between two points divided by the bandwidth. The default is exp( -abs(x)) yielding a normal kernel |
theta |
The bandwidth |
... |
Other arguments to be passed to the kernel function |
xwidth |
This has the same function as Mwidth but is in the scale of the x grid. If specified then Mwidth = xwidth/ ( grid$x[2]- grid$x[1]) |
ywidth |
This has the same function as Nwidth but is in the scale of the x grid. If specified then Nwidth = xwidth/ ( grid$x[2]- grid$x[1]) |
weights |
Weights to apply when smoothing. |
tol |
{ Tolerance for the weights of the N-W kernel. This avoids kernel estimates that are "far" away from data. Grid points with weights less than tol are set to NA. }
The function works by taking convolutions using an FFT. The missing pixels are taken into account and the kernel smoothing is correctly normalized following the classical Nadaraya-Watson estimator. If the kernel has limited support then the width arguments can be set to reduce the amount of computation. (See example below.)
The smoothed matrix.
image.smooth.setup, as.image
# first convert precip data to the 128X128 discretized image format ( with # missing values to indicate where data is not observed) # out<- as.image( RMprecip$y, x= RMprecip$x, nrow=128, ncol=128) # out$z is the image matrix dx<- out$x[2]- out$x[1] dy<- out$y[2] - out$y[1] # look<- image.smooth( out$z, dx=dx, dy=dy, theta= .25) # grid scale in degree and so kernel bandwidth is .25 degrees. image.plot( x= out$x, y=out$y, z= look) points( RMprecip$x) # to save on computation, decrease the padding with zeroes look<- image.smooth( out$z, dx=dx, dy=dy, theta= .25, Mwidth=32,Nwidth=32) # the range of these data is ~ 10 and so 32*( 10/128) = 2.5 # about 10 standard deviations of the normal kernel so there is still # lots of room for padding # a minimal choice might be Mwidth = 4* (.25/dx) 4 SD for the normal # # creating weighting object outside the call # this is useful when one wants to smooth different data sets but on the # same grid with the same kernel function # wght<- image.smooth.setup( nrow=128, ncol=128, dx=dx, dy=dy, theta= .25, Mwidth=32, Nwidth=32) # # random fields from smoothing white noise with this filter. # look<- image.smooth( matrix( rnorm(128**2), 128,128), wght) # take a look: image.plot( look)