I created following structural equation model from iris data set using lavaan package in R:
How do I interpret these numbers. The output (of sem() function of lavaan package) is given below. It did not give any P values:
lavaan (0.5-18) converged normally after 64 iterations Number of observations 150 Estimator ML Minimum Function Test Statistic NA Degrees of freedom -4 Minimum Function Value 0.0000000000000 Parameter estimates: Information Expected Standard Errors Standard Estimate Std.err Z-value P(>|z|) Latent variables: sepf =~ Sepal.Length 1.000 Sepal.Width -0.469 petf =~ Petal.Length 1.000 Petal.Width 0.507 lenf =~ Petal.Length 1.000 Sepal.Length -0.177 widf =~ Sepal.Width 1.000 strf =~ sepf 1.000 petf 2.084 bulkf =~ lenf 1.000 widf 0.579 Regressions: strf ~ Species 0.842 bulkf ~ Species 0.290 Covariances: strf ~~ bulkf 0.065 Variances: Sepal.Length 0.361 Sepal.Width 0.129 Petal.Length 0.231 Petal.Width 0.047 sepf -0.120 petf -0.220 lenf -0.179 widf 0.084 strf 0.053 bulkf -0.025 ----------------------------------------------- Warning messages: 1: In lav_data_full(data = data, group = group, group.label = group.label, : lavaan WARNING: unordered factor(s) with more than 2 levels detected in data: Species 2: In lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING: could not compute standard errors! lavaan NOTE: this may be a symptom that the model is not identified. 3: In lavaan::lavaan(model = model, data = mydf, model.type = "sem", : lavaan WARNING: some estimated variances are negative 4: In lavaan::lavaan(model = model, data = mydf, model.type = "sem", : lavaan WARNING: covariance matrix of latent variables is not positive definite; use inspect(fit,"cov.lv") to investigate. 5: In sqrt(ETA2) : NaNs produced 6: In sqrt(ETA2) : NaNs produced 7: In sqrt(ETA2) : NaNs produced > Do I just take large estimates as signficant? Thanks for your insight.
Best Answer
Moving comments above to an answer:
You really can't say anything at all about this model. On top of being completely unidentified it is badly broken (negative variance terms abound). So I would throw this model out and try something completely different.
Edit: This model is vastly over-identified and hence it is not unique (therefore, it's likelihood surface has no curvature, and no standard errors can be computed). The model converges to some location, but given some different starting values it will almost certainly converge to an entirely different location that fits equally well. Therefore, do not interpret this model at all, as it is largely meaningless, and lavaan surely printed a warning message saying that the model is probably not identified
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