Provided by: cmtk_2.1.0-1_amd64 bug


       glm - General Linear Model


       glm [options] ctlfile imgfile_pattern [ctlfile imgfile_pattern ...]


       Statistical modeling of pixel intensities in multiple images using a General Linear Model.


   Global Toolkit Options (these are shared by all CMTK tools)
            Write list of basic command line options to standard output.

            Write complete list of basic and advanced command line options to standard output.

            Write list of command line options to standard output in MediaWiki markup.

            Write man page source in 'nroff' markup to standard output.

            Write toolkit version to standard output.

            Write the current command line to standard output.

       --verbose-level <integer>
            Set verbosity level.

       --verbose, -v
            Increment verbosity level by 1 (deprecated; supported for backward compatibility).

       --threads <integer>
            Set maximum number of parallel threads (for POSIX threads and OpenMP).

   Main Options
       --exclude-constant, -x
            Exclude automatic constant parameter from model.

       --normalize, -n
            Normalize model parameters w.r.t. data variances.

       --exp, -e
            Use exponential model rather than linear model.

       --ignore-parameter <string>, -i <string>
            Ignore parameter with given NUMBER (0..n-1). Can be repeated.

       --select-parameter <string>, -s <string>
            Select parameter with given NAME for model. Can be repeated.

       --crop <string>, -c <string>
            To save space/time, crop images: x0,y0,z0,x1,y1,z2

       --output-pattern <string>, -O <string>
            Filename  pattern  for output (default: 'model_%s_%02d_%s.nii') with %d for parameter
            number [Default: model_%s_%02d_%s.nii ]


       Torsten Rohlfing, Michael P. Hasak, Greg Jefferis, Calvin R. Maurer, Daniel B. Russakoff



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       From April 2009 through September 2011, CMTK Development and Maintenance was supported  by
       the  National  Institute  of  Biomedical  Imaging  and  Bioengineering  under Grant No.R01
       EB008381 (PI: Torsten Rohlfing).