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| PLS_Toolbox Documentation: coadd | < cluster | coda_dw > |
coadd
Purpose
Reduce resolution through combination of adjacent variables or samples.
Synopsis
databin = coadd(data,bins,options)
Description
COADD is used to combine ("bin") adjacent variables, samples, or slabs of a matrix. Inputs include the original array data, the number of elements to combine together bins {default: 2}, and an optional options structure options.
Unpaired values at the end of the matrix are padded with the least biased value to complete the bin. Output is the co-added data. Unlike DERESOLV, COADD reduces the size of the data matrix by a factor of 1/bins for the dimension.
Example
Given a matrix, data, size 300 by 1000, the following would coadd variables in groups of three:
databin = coadd(data,3);
and the following would coadd samples in groups of two:
options.dim = 1;
databin = coadd(data,2,options);
The following is equivalent to the previous two lines.
databin = coadd(data,2,struct('dim',1));
Options
Algorithm
The three modes, sum, mean and prod behave according to the following (described in terms of variables):
SUM: groups of variables are added together and stored. The resulting values will be larger in magnitude than the original values by a factor equal to the number of variables binned.
MEAN: groups of variables are added together and that sum is divided by the number of variables binned. The resulting values will be similar in magnitude to the original values.
PROD: groups of variables are multiplied together.
See Also
| < cluster | coda_dw > |