# Naive Method with Exact Influence Remove a fixed percentage of samples (determined by the exact influence) at a time and refit the model to observe the change in the path of interest. Use method 1 - Naive Method with Exact Influence. ```r Test1( 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 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 `TestResult` containing: - **value**: The value of the parameter after dropping the influential points. - **points**: The indices of the most influential data points. - **methodname**: The name of the method used. - **testindex**: The index of the test performed. ## 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 max number of points to be dropped max_final <- ceiling(threshold * nrow(df) / 100) # perform rounding if necessary N <- nrow(df) # store number of observations in df for convenience # Determine whether parameter is negative or positive in order # to assess which direction to perturb it signFactor <- ifelse(par_value >= 0L, TRUE, FALSE) Test1_result <- Test1(df, model, var_one, var_two, PAR, threshold, fit, estimates, conc, int, par_value, max_final, N, signFactor) summary(Test1_result) ```

Test1 function