Usage: 1dSEM [options] -theta 1dfile -C 1dfile -psi 1dfile -DF nn.n
Computes path coefficients for connection matrix in Structural Equation
    Modeling (SEM)
 The program takes as input :
    1. A 1D file with an initial representation of the connection matrix
       with a 1 for each interaction component to be modeled and a 0 if
       if it is not to be modeled. This matrix should be PxP rows and column
    2. A 1D file of the C, correlation matrix, also with dimensions PxP
    3. A 1D file of the residual variance vector, psi
    4. The degrees of freedom, DF

    Output is printed to the terminal and may be redirected to a 1D file
    The path coefficient matrix is printed for each matrix computed
   -theta file.1D = connection matrix 1D file with initial representation
   -C file.1D = correlation matrix 1D file
   -psi file.1D = residual variance vector 1D file
   -DF nn.n = degrees of freedom
   -max_iter n = maximum number of iterations for convergence (Default=10000).
    Values can range from 1 to any positive integer less than 10000.
   -nrand n = number of random trials before optimization (Default = 100)
   -limits m.mmm n.nnn = lower and upper limits for connection coefficients
    (Default = -1.0 to 1.0)
   -calccost = no modeling at all, just calculate the cost function for the
    coefficients as given in the theta file. This may be useful for verifying
    published results
   -verbose nnnnn = print info every nnnnn steps

 Model search options:
 Look for best model. The initial connection matrix file must follow these
   specifications. Each entry must be 0 for entries excluded from the model,
   1 for each required entry in the minimum model, 2 for each possible path
   to try.
   -tree_growth or
   -model_search = search for best model by growing a model for one additional
    coefficient from the previous model for n-1 coefficients. If the initial
    theta matrix has no required coefficients, the initial model will grow from
    the best model for a single coefficient
   -max_paths n = maximum number of paths to include (Default = 1000)
   -stop_cost n.nnn = stop searching for paths when cost function is below
    this value (Default = 0.1)
   -forest_growth or
   -grow_all = search over all possible models by comparing models at
    incrementally increasing number of path coefficients. This
    algorithm searches all possible combinations; for the number of coeffs
    this method can be exceptionally slow, especially as the number of
    coefficients gets larger, for example at n>=9.
   -leafpicker = relevant only for forest growth searches. Expands the search
    optimization to look at multiple paths to avoid local minimum. This method
    is the default technique for tree growth and standard coefficient searches
 This program uses a Powell optimization algorithm to find the connection
   coefficients for any particular model.

   Powell, MJD, "The NEWUOA software for unconstrained optimization without
    derivatives", Technical report DAMTP 2004/NA08, Cambridge University
    Numerical Analysis Group:
    See:  http://www.ii.uib.no/~lennart/drgrad/Powell2004.pdf

   Bullmore, ET, Horwitz, B, Honey, GD, Brammer, MJ, Williams, SCR, Sharma, T,
    How Good is Good Enough in Path Analysis of fMRI Data?
    NeuroImage 11, 289-301 (2000)

   Stein, JL, et al., A validated network of effective amygdala connectivity,
    NeuroImage (2007), doi:10.1016/j.neuroimage.2007.03.022

 The initial representation in the theta file is non-zero for each element
   to be modeled. The 1D file can have leading columns for labels that will
   be used in the output. Label rows must be commented with the # symbol
 If using any of the model search options, the theta file should have a '1' for
   each required coefficient, '0' for each excluded coefficient, '2' for an
   optional coefficient. Excluded coefficients are not modeled. Required
   coefficients are included in every computed model.

 N.B. - Connection directionality in the path connection matrices is from
   column to row of the output connection coefficient matrices.

   Be very careful when interpreting those path coefficients.
   First of all, they are not correlation coefficients. Suppose we have a
   network with a path connecting from region A to region B. The meaning
   of the coefficient theta (e.g., 0.81) is this: if region A increases by
   one standard deviation from its mean, region B would be expected to increase
   by 0.81 its own standard deviations from its own mean while holding all other
   relevant regional connections constant. With a path coefficient of -0.16,
   when region A increases by one standard deviation from its mean, region B
   would be expected to decrease by 0.16 its own standard deviations from its
   own mean while holding all other relevant regional connections constant.

   So theoretically speaking the range of the path coefficients can be anything,
   but most of the time they range from -1 to 1. To save running time, the
   default values for -limits are set with -1 and 1, but if the result hits
   the boundary, increase them and re-run the analysis.

   To confirm a specific model:
    1dSEM -theta inittheta.1D -C SEMCorr.1D -psi SEMvar.1D -DF 30
   To search models by growing from the best single coefficient model
     up to 12 coefficients
    1dSEM -theta testthetas_ms.1D -C testcorr.1D -psi testpsi.1D \
    -limits -2 2 -nrand 100 -DF 30 -model_search -max_paths 12
   To search all possible models up to 8 coefficients:
    1dSEM -theta testthetas_ms.1D -C testcorr.1D -psi testpsi.1D \
    -nrand 10 -DF 30 -stop_cost 0.1 -grow_all -max_paths 8 | & tee testgrow.txt

   For more information, see https://afni.nimh.nih.gov/sscc/gangc/PathAna.html
    and our HBM 2007 poster at
 If you find this program useful, please cite:
   G Chen, DR Glen, JL Stein, AS Meyer-Lindenberg, ZS Saad, RW Cox,
   Model Validation and Automated Search in FMRI Path Analysis:
   A Fast Open-Source Tool for Structural Equation Modeling,
   Human Brain Mapping Conference, 2007