AFNI program: 3dDeconvolve

Output of -help

Program:          3dDeconvolve 
Author:           B. Douglas Ward, et al. 
Initial Release:  02 September 1998 
Latest Revision:  03 August 2004 

Program to calculate the deconvolution of a measurement 3d+time dataset    
with a specified input stimulus time series.  This program will also       
perform multiple linear regression using multiple input stimulus time      
series. Output consists of an AFNI 'bucket' type dataset containing the    
least squares estimates of the linear regression coefficients, t-statistics
for significance of the coefficients, partial F-statistics for significance
of the individual input stimuli, and the F-statistic for significance of   
the overall regression.  Additional output consists of a 3d+time dataset   
containing the estimated system impulse response function.                 
     Input data and control options:                                   
-input fname         fname = filename of 3d+time input dataset         
[-input1D dname]     dname = filename of single (fMRI) .1D time series 
[-nodata]            Evaluate experimental design only (no input data) 
[-mask mname]        mname = filename of 3d mask dataset               
[-censor cname]      cname = filename of censor .1D time series        
[-concat rname]      rname = filename for list of concatenated runs    
[-nfirst fnum]       fnum = number of first dataset image to use in the
                       deconvolution procedure. (default = max maxlag) 
[-nlast  lnum]       lnum = number of last dataset image to use in the 
                       deconvolution procedure. (default = last point) 
[-polort pnum]       pnum = degree of polynomial corresponding to the  
                       null hypothesis  (default: pnum = 1)            
[-legendre]          use Legendre polynomials for null hypothesis      
[-nolegendre]        use power polynomials for null hypotheses         
                       (default is -legendre)                          
[-nocond]            don't calculate matrix condition number           
[-svd]               Use SVD instead of Gaussian elimination (default) 
[-nosvd]             Use Gaussian elimination instead of SVD           
[-rmsmin r]          r = minimum rms error to reject reduced model     
     Input stimulus options:                                           
-num_stimts num      num = number of input stimulus time series        
                       (0 <= num)   (default: num = 0)                 
-stim_file k sname   sname = filename of kth time series input stimulus
[-stim_label k slabel] slabel = label for kth input stimulus           
[-stim_base k]       kth input stimulus is part of the baseline model  
[-stim_minlag k m]   m = minimum time lag for kth input stimulus       
                       (default: m = 0)                                
[-stim_maxlag k n]   n = maximum time lag for kth input stimulus       
                       (default: n = 0)                                
[-stim_nptr k p]     p = number of stimulus function points per TR     
                       Note: This option requires 0 slice offset times 
                       (default: p = 1)                                
     General linear test (GLT) options:                                
-num_glt num         num = number of general linear tests (GLTs)       
                       (0 <= num)   (default: num = 0)                 
[-glt s gltname]     Perform s simultaneous linear tests, as specified 
                       by the matrix contained in file gltname         
[-glt_label k glabel]  glabel = label for kth general linear test      
[-gltsym gltname]    Read the GLT with symbolic names from the file    
     Options for output 3d+time datasets:                              
[-iresp k iprefix]   iprefix = prefix of 3d+time output dataset which  
                       will contain the kth estimated impulse response 
[-tshift]            Use cubic spline interpolation to time shift the  
                       estimated impulse response function, in order to
                       correct for differences in slice acquisition    
                       times. Note that this effects only the 3d+time  
                       output dataset generated by the -iresp option.  
[-sresp k sprefix]   sprefix = prefix of 3d+time output dataset which  
                       will contain the standard deviations of the     
                       kth impulse response function parameters        
[-fitts  fprefix]    fprefix = prefix of 3d+time output dataset which  
                       will contain the (full model) time series fit   
                       to the input data                               
[-errts  eprefix]    eprefix = prefix of 3d+time output dataset which  
                       will contain the residual error time series     
                       from the full model fit to the input data       
     Options to control the contents of the output bucket dataset:     
[-fout]            Flag to output the F-statistics                     
[-rout]            Flag to output the R^2 statistics                   
[-tout]            Flag to output the t-statistics                     
[-vout]            Flag to output the sample variance (MSE) map        
[-nobout]          Flag to suppress output of baseline coefficients    
                     (and associated statistics)                       
[-nocout]          Flag to suppress output of regression coefficients  
                     (and associated statistics)                       
[-full_first]      Flag to specify that the full model statistics will 
                     appear first in the bucket dataset output         
[-bucket bprefix]  Create one AFNI 'bucket' dataset containing various 
                     parameters of interest, such as the estimated IRF 
                     coefficients, and full model fit statistics.      
                     Output 'bucket' dataset is written to bprefix.    
[-xsave]           Flag to save X matrix into file bprefix.xsave       
                     (only works if -bucket option is also given)      
[-noxsave]         Don't save X matrix (this is the default)           
[-cbucket cprefix] Save the regression coefficients (no statistics)    
                     into a dataset named 'cprefix'.  This dataset     
                     will be used in a -xrestore run instead of the    
                     bucket dataset, if possible.                      
[-xrestore f.xsave] Restore the X matrix, etc. from a previous run     
                     that was saved into file 'f.xsave'.  You can      
                     then carry out new -glt tests.  When -xrestore    
                     is used, most other command line options are      
     The following options control the screen output only:             
[-quiet]             Flag to suppress most screen output               
[-xout]              Flag to write X and inv(X'X) matrices to screen   
[-xjpeg filename]    Write a JPEG file graphing the X matrix           
[-progress n]        Write statistical results for every nth voxel     
[-fdisp fval]        Write statistical results for those voxels        
                       whose full model F-statistic is > fval          

 -jobs J   Run the program with 'J' jobs (sub-processes).
             On a multi-CPU machine, this can speed the
             program up considerably.  On a single CPU
             machine, using this option is silly.
             J should be a number from 1 up to the
             number of CPU sharing memory on the system.
             J=1 is normal (single process) operation.
             The maximum allowed value of J is 32.
         * For more information on parallelizing, see
         * Use -mask to get more speed; cf. 3dAutomask.

** NOTE **
This version of the program has been compiled to use
double precision arithmetic for most internal calculations.

This page generated on Tue Aug 3 16:42:44 EDT 2004