View source: R/gen.sens.pars.R

gen.sens.pars | R Documentation |

This function can generate a set of path coefficients from a phantom variable to variables in a structural equation model based on given distributions of the rank of optimization target (with probability of using a distribution based on its rank).

gen.sens.pars( dist.mean, dist.rank, n.of.ants, nl, q = 1e-04, k = 500, xi = 0.5 )

`dist.mean` |
List of means - coordinates |

`dist.rank` |
Rank of the archived values of objective function |

`n.of.ants` |
Number of ants used in each iteration after the initialization of k converged sensitivity analysis models, default value is 10. |

`nl` |
Neighborhood of the search area |

`q` |
Locality of the search (0,1), default is 0.0001. |

`k` |
Size of the solution archive, default is 100. |

`xi` |
Convergence pressure (0, Inf), suggested: (0,1), default is 0.5. |

Generated sensitivity parameter values (i.e., a matrix with n.of.ants rows and n.of.sens.pars columns)

Leite, W., & Shen, Z., Marcoulides, K., Fish, C., & Harring, J. (in press). Using ant colony optimization for sensitivity analysis in structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal.

Socha, K., & Dorigo, M. (2008). Ant colony optimization for continuous domains. European Journal of Operational Research, 185(3), 1155-1173.

We thank Dr. Krzysztof Socha for providing us the original code (http://iridia.ulb.ac.be/supp/IridiaSupp2008-001/) for this function.

k <- 50 # size of archive # Generate dist.mean and dist.rank dist.mean <- cbind(rnorm(k), rnorm(k), rnorm(k), rnorm(k), rnorm(k)) y <- rowMeans(dist.mean) dist.rank <- rank(-y, ties.method = "random") # set up neighborhood nl <- matrix(NA, k, k-1) for (i in 1:k){ nl[i,] <- (1:k)[1:k != i] } my.sens.pars <- gen.sens.pars(dist.mean, dist.rank, n.of.ants = 10, nl, q = 0.0001, k =50, xi = 0.50) my.sens.pars

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