gen.sens.pars {SEMsens}

Generate Sensitivity Parameters

Description

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).

Usage

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

Arguments

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.

Value

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

References

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.

Examples

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