sa.sa | R Documentation |

This function can perform sensitivity analysis for structural equation modeling using simulated annealing (SA)

sa.sa( data = NULL, sample.cov, sample.nobs, model, sens.model, opt.fun = 1, d = NULL, paths = NULL, verbose = TRUE, n.iter = 10, e = 1e-10, k = 10, sig.level = 0.05, Ntemps = 10, C.criteria = 1, steepness = 6, measurement = FALSE )

`data` |
The data set used for analysis. |

`sample.cov` |
covariance matrix for SEM analysis when data are not available. |

`sample.nobs` |
Number of observations for covariance matrix. |

`model` |
The analytic model of interest. |

`sens.model` |
Sensitivity analysis model template for structural equation modeling with a phantom variable. This is the model of interest with a phantom variable and sensitivity parameters added. See examples provided. |

`opt.fun` |
Customized or preset optimization function. The argument can be customized as a function, e.g., opt.fun = quote(new.par$pvalue[paths]-old.par$pvalue[paths]), where new.par and old.par are the parameter estimates from the sensitivity analysis and analytic models, respectively. When opt.fun is 1, the optimization function is the average departure of new estimate from the old estimate divided by the old estimate y <- mean(abs(new.par$est.std[paths] - old.par$est.std[paths]))/mean(abs(old.par$est.std[paths])); When opt.fun is 2, the optimization function is the standard deviation of deviance divided by the old estimate y <- stats::sd(new.par$est.std[paths] - old.par$est.std[paths])/ mean(abs(old.par$est.std[paths])); When opt.fun is 3, the optimization function is the average p value changed or y <- mean(abs(new.par$pvalue[paths] - old.par$pvalue[paths])); When opt.fun is 4, the optimization function is the average distance from significance level or y <- mean(abs(new.par$pvalue[paths] - rep(sig.level,length(paths)))); When opt.fun is 5, we assess the change of RMSEA or y <- abs(unname(lavaan::fitmeasures(new.out)["rmsea"]) - unname(lavaan::fitmeasures(old.out)["rmsea"])); When opt.fun is 6, we optimize how close RMSEA is to 0.05 or y <- 1/abs(unname(lavaan::fitmeasures(new.out)["rmsea"]) - 0.05). |

`d` |
Domains for initial sampling, default is c(-1 ,1) for all sensitivity analysis parameters. |

`paths` |
Paths in the model to be evaluated in a sensitivity analysis. If not specified, all paths will be evaluated. It can be specified in a numeric format or in a model format. For example, if we evaluate the changes (in p value or parameter estimation) for paths in an analytic model, we may specify paths in a model format, e.g., paths = 'm ~ x y ~ x + m'. Or, alternatively, as specify paths = c(1:3) if these paths present in line 1 to 3 in the sensitivity analysis model results. |

`verbose` |
Print out evaluation process if TRUE, default is TRUE. |

`n.iter` |
Maximal number of function evaluations within each temperature. |

`e` |
Maximum error value used when solution quality used as the stopping criterion, default is 1e-10. |

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

`sig.level` |
Significance level, default value is 0.05. |

`Ntemps` |
Number of temperatures that the algorithm visits. Default value is 10. |

`C.criteria` |
Convergence criterion. Default value is 1. |

`steepness` |
Steepness of cooling schedule. Default value is 6. |

`measurement` |
Logical. If TRUE, the argument paths will include measurement paths in the lavaanify format. Default is FALSE. |

Sensitivity analysis results, including the number of evaluations (n.eval), number of iterations (n.iter), the maximum value of the objective function (max.y) and associated sensitivity parameters values (phantom.coef), analytic model (old.model), its results (old.model.par) and fit measures (old.model.fit), sensitivity analysis model (sens.model), its fit measures (sens.fit), outcome of the objective function (outcome), sensitivity parameters across all converged evaluations (sens.pars), sensitivity analysis model results (model.results), analytic model results (old.out), and the first converged sensitivity analysis model results (sens.out).

Fisk, C., Harring, J., Shen, Z., Leite, W., Suen, K., & Marcoulides, K. (2022). Using simulated annealing to investigate sensitivity of SEM to external model misspecification. Educational and Psychological Measurement. <doi:10.1177/00131644211073121>

library(lavaan) # Generate data, this is optional as lavaan also takes variance covariance matrix sim.model <- ' x =~ x1 + 0.8*x2 + 1.2*x3 y =~ y1 + 0.5*y2 + 1.5*y3 m ~ 0.5*x y ~ 0.5*x + 0.8*m' set.seed(10) data <- simulateData(sim.model, sample.nobs = 1000L) # standardize dataset data = data.frame(apply(data,2,scale)) # Step 1: Set up the analytic model of interest model <- 'x =~ x1 + x2 + x3 y =~ y1 + y2 + y3 m ~ x y ~ x + m' # Step 2: Set up the sensitivity analysis model. # The sensitivity parameters are phantom1, phantom2, and phantom3 in this example. sens.model = 'x =~ x1 + x2 + x3 y =~ y1 + y2 + y3 m ~ x y ~ x + m x ~ phantom1*phantom m ~ phantom2*phantom y ~ phantom3*phantom phantom =~ 0 # added for mean of zero phantom ~~ 1*phantom' # added for unit variance # Step 3: Set up the paths of interest to be evaluated in sensitivity analysis. # Suppose we are interested in all direct and indirect paths. paths <- 'm ~ x y ~ x + m' # Step 4: Perform sensitivity analysis mysa <- sa.sa(data = data, model = model, sens.model = sens.model, paths = paths, n.iter = 3, Ntemps = 2) # We set Ntemps = 2 and n.iter = 3 to reduce the running time. # You may leave them as default values or specify larger numbers.

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