fields (version 1.7.2)

image.smooth: Kernel smoother for irregular 2-d data

Description

Takes an image matrix and applies a kernel smoother to it. Missing values are handled using the Nadaraya/Watson normalization of the kernel.

Usage

image.smooth
 (Y, 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)

Value

The smoothed matrix.

Details

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.)

See Also

image.smooth.setup, as.image

Examples

# 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)