Solved – How to interpret this lavaan structural equation model

I created following structural equation model from iris data set using lavaan package in R:

enter image description here

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,"") 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.

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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