as.image: Creates image from irregular x,y,z

Description

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.

Usage

as.image(Z, ind=NULL, grid=NULL, x=NULL, nrow=64, ncol=64,weights=NULL,
 na.rm=FALSE, nx=NULL,ny=NULL, boundary.grid=FALSE)

Arguments

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.

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)

Examples

Run this code

# 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 -- 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