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.
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
Same as nx this is depreciated.
ncol
Same as ny this is depreciated.
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
Number of grid point in X coordinate.
ny
Number of grid points in Y coordinate.
boundary.grid
If FALSE grid points are assumed to be the
grid midpoints. If TRUE they are the grid box boundaries.
FUN
The function to apply to common values in a grid box.
The default is a mean (or weighted mean). If FUN is specified the
weights are not used.
Details
The discretization is straightforward once the grid is determined.
If two or more Z values have locations in the same cell the weighted
average value is taken as the value. The weights component that is
returned can be used to account for means that have different numbers
(or precisions) of observations contributing to the grid point averages.
The default weights are taken to be one for each observation.
See the source code to modify this to get more
information about coincident locations. (See the call to fast.1way)
Value
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 weights is an image matrix with the sum of the
individual weights for each cell. If no weights are specified the
default for each observation is one and so the weights will be the
number of observations in each bin.
# convert precip data to 50X50 image
look<- as.image( RMprecip$y, x= RMprecip$x, nx=50, ny=50)
image.plot( look)
# number of obs in each cell -- in this case equal to the
# aggregated weights because each obs had equal wieght in the call
image.plot( look$x ,look$y, look$weights, col=terrain.colors(50))
# hot spot is around Denver