permuteMeasEq function.PT:data.frame returned by a call to parTable on the constrained modelmodelType:modelType in the call to permuteMeasEqANOVA:AFI.obs:AFI.dist:data.frame with n.Permutations rows and one column for each AFI.obs.AFI.pval:AFI.obs) calculated using slot AFI.dist, indicating the probability of observing a change at least as extreme as AFI.obs if the null hypothesis were trueMI.obs:data.frame of observed Lagrange Multipliers (modification indices) associated with the equality constraints or fixed parameters specified in the param argument. This is a subset of the output returned by a call to lavTestScore on the constrained model.MI.dist:MI.obs$X2) at each permutation of group assignment or of covariatesextra.obs:permuteMeasEq was called with an extra function, the output when applied to the original data is concatenated into this vectorextra.dist:data.frame, each column of which contains the permutation distribution of the corresponding statistic in slot extra.obsn.Permutations:n.Converged:n.nonConverged:n.Permutations indicating how many times group assignment was randomly permuted (at each iteration) before converging on a solutionn.Sparse:ordered indicators when modelType == "mgcfa". A vector of length n.Permutations indicating how many times group assignment was randomly permuted (at each iteration) before obtaining a sample with all categories observed in all groupsoldSeed:.Random.seed before running permuteMeasEq. Only relevant when using a parallel/multicore option and the original RNGkind() != "L'Ecuyer-CMRG". This enables users to restore their previous .Random.seed state, if desired, by running: .Random.seed[-1] <- permutedResults@oldSeed[-1]signature(object = "permuteMeasEq"): The show function is used to summarize the results of the multiparameter omnibus test of measurement equivalence, using the user-specified AFIs. The parametric chi-squared (difference) test is also displayed.signature(object = "permuteMeasEq", alpha = .05, nd = 3, extra = FALSE): The summary function prints the same information from the show method, but when extra = FALSE (the default) it also provides a table summarizing any requested follow-up tests of DIF using modification indices in slot MI.obs. The user can also specify an alpha level for flagging modification indices as significant, as well as nd (the number of digits displayed). For each modification index, the p value is displayed using a central chi-squared distribution with the df shown in that column. Additionally, a p value is displayed using the permutation distribution of the maximum index, which controls the familywise Type I error rate in a manner similar to Tukey's studentized range test. If any indices are flagged as significant using the tukey.p.value, then a message is displayed for each flagged index. The invisibly returned data.frame is the displayed table of modification indices, unless permuteMeasEq was called with param = NULL, in which case the invisibly returned object is object. If extra = TRUE, the permutation-based p values for each statistic returned by the extra function are displayed and returned in a data.frame instead of the modification indices requested in the param argument.signature(x = "permuteMeasEq", ..., AFI, alpha = .05, nd = 3, printLegend = TRUE, legendArgs = list(x = "topleft")): The hist function provides a histogram for the permutation distribution of the specified AFI, including observed and critical values from the specified alpha level. Distributions of modification indices and any extra output are not available with this method, but they can be created manually by accessing the distributions in slot MI.dist or extra.dist. The user can also specify additional graphical parameters to hist via ..., as well as pass a list of arguments to an optional legend via legendArgs. If AFI = "chisq", then the probability density and critical value from the central chi-squared distribution are also included in the plot. If the user wants more control over customization, hist returns a list of length == 2, containing the arguments for the call to hist and the arguments to the call for legend, respectively. This list may facilitate creating a customized histogram of AFI.dist, MI.dist, or extra.dist.permuteMeasEq
# See the example from the permuteMeasEq function
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