| as.image {fields} | R Documentation |
Discretizes a set of 2-d locations to a grid and produces a image object with the z values in the right cells. For cells with more than one Z value the average is used.
as.image(Z, ind=NULL, grid=NULL, x=NULL, nrow=64, ncol=64,weights=NULL, na.rm=FALSE, nx=NULL,ny=NULL)
Z |
Values of image |
ind |
A matrix giving the row and column subscripts for each image value in Z. (Not needed if x is specified.) |
grid |
A list with components x and y of equally spaced values describing the centers of the grid points. The default is to use nrow and ncol and the ranges of the data locations (x) to construct a grid. |
x |
Locations of image values. Not needed if ind is specified. |
nrow |
Number of rows in image matrix ( x-axis direction) |
ncol |
Number of columns in image matrix ( y-axis direction) |
weights |
If two or more values fall into the same pixel a weighted average is used to represent the pixel value. Default is equal weights. |
na.rm |
If true NA's are removed from the Z vector. |
nx |
Same as nrow |
ny |
Same as ncol |
The discretization is straightforward once the grid is determined. If two or more Z values have locations in the same cell the average value is taken as the value. See the source code to modify this to get more information about coincident locations. (See the call to fast.1way)
An list in image format with a few more components. Components x and y are the grid values , z is a nrow X ncol matrix with the Z values. NA's are placed at cell locations where Z data has not been supplied. Component ind is a 2 column matrix with subscripts for the locations of the values in the image matrix. Component N is an image matrix with the number of original values that fall within each grid box. Component weights is an image matrix with the sum of the individual weights for each cell.
image.smooth, image.plot, Krig.discretize, Krig.replicates
# convert precip data to 50X50 image look<- as.image( RMprecip$y, x= RMprecip$x, nrow=50, ncol=50) image.plot( look) # number of obs in each cell. image.plot( look$x ,look$y, look$N, col=terrain.colors(50)) # hot spot is around Denver