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1dSEM.json
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1dSEM.json
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{
"command-line": "1dSEM [THETA] [C] [PSI] [DF] [MAX_ITER] [NRAND] [LIMITS] [CALCCOST] [VERBOSE] [TREE_GROWTH] [MODEL_SEARCH] [MAX_PATHS] [STOP_COST] [FOREST_GROWTH] [GROW_ALL] [LEAFPICKER]",
"description": "tool description",
"inputs": [
{
"command-line-flag": "-theta",
"description": "-theta file.1D = connection matrix 1D file with initial representation",
"id": "THETA",
"name": "THETA",
"optional": true,
"type": "String",
"value-key": "[THETA]"
},
{
"command-line-flag": "-C",
"description": "-C file.1D = correlation matrix 1D file",
"id": "C",
"name": "C",
"optional": true,
"type": "String",
"value-key": "[C]"
},
{
"command-line-flag": "-psi",
"description": "-psi file.1D = residual variance vector 1D file",
"id": "PSI",
"name": "PSI",
"optional": true,
"type": "String",
"value-key": "[PSI]"
},
{
"command-line-flag": "-DF",
"description": "-DF nn.n = degrees of freedom",
"id": "DF",
"name": "DF",
"optional": true,
"type": "String",
"value-key": "[DF]"
},
{
"command-line-flag": "-max_iter",
"description": "maximum number of iterations for convergence (Default=10000). Values can range from 1 to any positive integer less than 10000.",
"id": "MAX_ITER",
"name": "MAX_ITER",
"optional": true,
"type": "String",
"value-key": "[MAX_ITER]"
},
{
"command-line-flag": "-nrand",
"description": "number of random trials before optimization (Default = 100)",
"id": "NRAND",
"name": "NRAND",
"optional": true,
"type": "String",
"value-key": "[NRAND]"
},
{
"command-line-flag": "-limits",
"description": "-limits m.mmm n.nnn = lower and upper limits for connection coefficients (Default = -1.0 to 1.0)",
"id": "LIMITS",
"name": "LIMITS",
"optional": true,
"type": "String",
"value-key": "[LIMITS]"
},
{
"command-line-flag": "-calccost",
"description": "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",
"id": "CALCCOST",
"name": "CALCCOST",
"optional": true,
"type": "String",
"value-key": "[CALCCOST]"
},
{
"command-line-flag": "-verbose",
"description": "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.",
"id": "VERBOSE",
"name": "VERBOSE",
"optional": true,
"type": "String",
"value-key": "[VERBOSE]"
},
{
"command-line-flag": "-tree_growth",
"description": "-tree_growth or",
"id": "TREE_GROWTH",
"name": "TREE_GROWTH",
"optional": true,
"type": "String",
"value-key": "[TREE_GROWTH]"
},
{
"command-line-flag": "-model_search",
"description": "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",
"id": "MODEL_SEARCH",
"name": "MODEL_SEARCH",
"optional": true,
"type": "String",
"value-key": "[MODEL_SEARCH]"
},
{
"command-line-flag": "-max_paths",
"description": "maximum number of paths to include (Default = 1000)",
"id": "MAX_PATHS",
"name": "MAX_PATHS",
"optional": true,
"type": "String",
"value-key": "[MAX_PATHS]"
},
{
"command-line-flag": "-stop_cost",
"description": "-stop_cost n.nnn = stop searching for paths when cost function is below this value (Default = 0.1)",
"id": "STOP_COST",
"name": "STOP_COST",
"optional": true,
"type": "String",
"value-key": "[STOP_COST]"
},
{
"command-line-flag": "-forest_growth",
"description": "-forest_growth or",
"id": "FOREST_GROWTH",
"name": "FOREST_GROWTH",
"optional": true,
"type": "String",
"value-key": "[FOREST_GROWTH]"
},
{
"command-line-flag": "-grow_all",
"description": "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.",
"id": "GROW_ALL",
"name": "GROW_ALL",
"optional": true,
"type": "String",
"value-key": "[GROW_ALL]"
},
{
"command-line-flag": "-leafpicker",
"description": "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. References: Powell, MJD, \"The NEWUOA software for unconstrained optimization without derivatives\", Technical report DAMTP 2004/NA08, Cambridge University Numerical Analysis Group -- http://www.damtp.cam.ac.uk/user/na/reports.html 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. Examples: 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 \\",
"id": "LEAFPICKER",
"name": "LEAFPICKER",
"optional": true,
"type": "String",
"value-key": "[LEAFPICKER]"
}
],
"name": "tool name",
"schema-version": "0.5",
"suggested-resources": {
"cpu-cores": 1,
"ram": 1,
"walltime-estimate": 60
},
"tags": {},
"tool-version": "v0.1.0"
}