# Use Particle Swarm Optimization (PSO) to Try to Switch Sign of Parameter This function uses the Particle Swarm Optimization (PSO) method to iteratively remove data points in order to switch the sign of a specific path in a Structural Equation Modeling (SEM) model. ```r Test12( df, model, var_one, var_two, PAR, threshold, fit, estimates, conc, int, par_value, max_final, N, signFactor, ... ) ``` ## Arguments - `df`: A data frame containing the dataset. - `model`: A specified SEM model. - `var_one`: The first variable of interest. - `var_two`: The second variable of interest. - `PAR`: The path of interest. - `threshold`: The threshold for the percentage of data dropped. - `fit`: The SEM object. - `estimates`: The estimates from the SEM model. - `conc`: A data frame containing the parameter of interest. - `int`: The value of the path of interest. - `par_value`: The original value of the parameter of interest. - `max_final`: The maximum number of influential data points to consider. - `N`: The total number of data points. - `signFactor`: A factor indicating the direction of parameter change (positive or negative). - `...`: Other arguments. ## Returns A list of class `TestResult12` containing: - **psoDrops**: The indices of the most influential data points selected by the PSO method. - **initialValue**: The original value of the parameter. - **finalValue**: The final value of the parameter after applying the PSO method. - **methodname**: The name of the method used. - **testindex**: The index of the test performed. - **PAR**: The path of interest that was evaluated. - **threshold**: The threshold used for the percentage of dropped points. - **N**: The total number of data points. - **max_final**: The maximum number of points allowed to be dropped. - **par_value**: The original value of the parameter. ## Examples ```r library(lavaan) library(dplyr) library(semfindr) library(R.utils) # Import data df <- PoliticalDemocracy # Build Model model <- ' # measurement model ind60 =~ x1 + x2 + x3 dem60 =~ y1 + y2 + y3 + y4 dem65 =~ y5 + y6 + y7 + y8 # regressions dem60 ~ ind60 dem65 ~ ind60 + dem60 # residual correlations y1 ~~ y5 y2 ~~ y4 + y6 y3 ~~ y7 y4 ~~ y8 y6 ~~ y8 ' var_one <- 'dem65' # first term var_two <- 'ind60' # second term PAR <- c("dem65~ind60") # full relation threshold <- 10 # Fit SEM model fit <- lavaan::sem(model, data = df) summary(fit) # Get Estimates of Parameters from SEM estimates <- parameterEstimates(fit) # Determine the value of the parameter of interest conc <- data.frame(lhs = estimates$lhs, rhs = estimates$rhs, est = estimates$est) int <- conc %>% filter(lhs == var_one & rhs == var_two) par_value <- int$est # this is the value of the parameter of interest # Compute the max number of points to be dropped max_final <- ceiling(threshold * nrow(df) / 100) # perform rounding if necessary N <- nrow(df) # store the number of observations in df for convenience # Determine whether the parameter is negative or positive in order # to assess which direction to perturb it signFactor <- ifelse(par_value >= 0L, TRUE, FALSE) Test12_result = Test12(df, model, var_one, var_two, PAR, threshold, fit, estimates, conc, int, par_value, max_final, N, signFactor) summary(Test12_result) ```

Test12 function