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\name{as.image}
\alias{as.image}
\title{
  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,weights=rep(1, length(Z)),
 na.rm=FALSE, nx=64, ny=64, boundary.grid=FALSE,  nrow=NULL, ncol=NULL,
 FUN = NULL)
}
\arguments{
\item{Z}{
Values of image. 
}
\item{ind}{
A matrix giving the row and column subscripts for each image 
value in Z. (Not needed if x is specified.) 
}
\item{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.  
}
\item{x}{
Locations of image values. Not needed if  ind  is specified.  
}
\item{nrow}{
Same as nx this is depreciated. 
}
\item{ncol}{
Same as ny this is depreciated.  
}
\item{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. 
}
\item{na.rm}{ 
If true NA's are removed from the Z vector.}
\item{nx}{Number of grid point in X coordinate.}
\item{ny}{Number of grid points in Y coordinate.}
\item{boundary.grid}{If FALSE grid points are assumed to be the 
grid midpoints. If TRUE they are the grid box boundaries.}
\item{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. }
}

\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) 

}
\seealso{
image.smooth, image.plot, Krig.discretize, Krig.replicates  
}
\examples{
# convert precip data to 50X50 image  
look<- as.image( RMprecip$y, x= RMprecip$x, nx=50, ny=50)
image.plot( look) 

# reduced grid extent compared to the domain
gridList<- list( x = seq(-105,-101,length.out=10),
                 y = seq(  38, 42,length.out=10) )
look2<- as.image( RMprecip$y, x= RMprecip$x,grid=gridList)
image.plot( look2) 

# number of obs in each cell -- in this case equal to the 
# aggregated weights because each obs had equal weight in the call

image.plot( look$x ,look$y, look$weights, col=terrain.colors(50)) 
# hot spot is around Denver
}
\keyword{manip}
% docclass is function
% Converted by Sd2Rd version 1.21.