MLR

[knickodem]

estimator = "MLR" throws an error in the CFA section (SEs and test statistics are not calculated in the EFA section). Occurs if either of se = "robust.mlr" or test = "yuan.bentler" are selected.

Error in t.default(Y) : argument is not a matrix
17. t.default(Y)
16. t(Y)
15. t(t(Y) - Mu)
14. lav_mvnorm_scores_vech_sigma(Y = Y, wt = wt, Mu = Mu, Sigma = Sigma, 
      Sinv.method = Sinv.method, Sigma.inv = Sigma.inv)
13. lav_mvnorm_information_firstorder(Y = lavdata@X[[g]], Mu = MEAN, 
      Sigma = lavimplied$cov[[g]], wt = WT, cluster.idx = cluster.idx, 
      x.idx = lavsamplestats@x.idx[[g]], meanstructure = lavmodel@meanstructure)
12. lav_model_h1_information_firstorder(lavmodel = lavmodel, lavsamplestats = lavsamplestats, 
      lavdata = lavdata, lavoptions = lavoptions, lavimplied = lavimplied, 
      lavh1 = lavh1, lavcache = lavcache)
11. lav_model_information_firstorder(lavmodel = lavmodel, lavsamplestats = lavsamplestats, 
      lavdata = lavdata, lavcache = lavcache, lavimplied = lavimplied, 
      lavh1 = lavh1, lavoptions = lavoptions2, extra = TRUE, check.pd = FALSE, 
      augmented = FALSE, inverted = FALSE, use.ginv = use.ginv)
10. lav_model_nvcov_robust_sandwich(lavmodel = lavmodel, lavsamplestats = lavsamplestats, 
      lavdata = lavdata, lavcache = lavcache, lavimplied = lavimplied, 
      lavh1 = lavh1, lavoptions = lavoptions, use.ginv = use.ginv)
9. lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, 
      lavoptions = lavoptions, lavdata = lavdata, lavpartable = lavpartable, 
      lavcache = lavcache, lavimplied = lavimplied, lavh1 = lavh1)
8. lavaan::lavaan(model = syntax[[c]], sample.cov = sample.cov, 
      sample.mean = sample.mean, sample.th = sample.th, sample.nobs = sample.nobs, 
      WLS.V = WLS.V, NACOV = NACOV, estimator = estimator, parameterization = "delta", 
      model.type = "cfa", int.ov.free = TRUE, int.lv.free = FALSE, ...
7. eval(mc, parent.frame())
6. eval(mc, parent.frame())
5. lavaan::cfa(model = syntax[[c]], sample.cov = sample.cov, sample.nobs = sample.nobs, 
      sample.th = sample.th, sample.mean = sample.mean, WLS.V = WLS.V, 
      NACOV = NACOV, estimator = estimator, parameterization = "delta") at k_cfa.R#45
4. (function (syntax, variables, ordered, estimator, missing, ...) {
      sampstats <- lavaan::lavCor(object = variables, ordered = ordered, 
      estimator = estimator, missing = missing, output = "fit", ...
3. do.call(kfa:::k_cfa, args = c(list(syntax = cfa.syntax[[fold]], 
variables = variables[testfolds[[fold]], ]), temp.lavaan.args[-1]))
2. FUN(X[[i]], ...)
1. lapply(1:k, function(fold) {
do.call(kfa:::k_cfa, args = c(list(syntax = cfa.syntax[[fold]], 
variables = variables[testfolds[[fold]], ]), temp.lavaan.args[-1]))
})