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. library model <-tidy_sem (iris, "\\.") model <-measurement (model) res <-estimate_lavaan (model) #> Warning: lavaan WARNING: some estimated ov variances are negative . 对于实际操作，可以这样在一定程度上缓解问题 sem (mod,df,optim.method="BFGS",optim.force.converged=T,check.post= F）. What is the . I guess the problem might be the correlation between two . In a first step, we thus need to create a specific function that defines the latent measurement models for the latent measures specified as x and y in run_specs () and incoporate them into a formula that follows the lavaan-syntax. Warning in Lavaan, variance-covariance not positive ... - ResearchGate Estimate Std.Err z-value P(>|z|) Std.lv Std.all .Competence -0.188 0.105 -1.796 0.073 -0.324 -0.324 and it is giving the warning: lavaan WARNING: some estimated lv variances are negative Model i am using: If your counts are lower (e.g., mean of 10 or lower), then you probably have predicted values that are negative, which makes no sense for counts. There are several freely available packages for structural equation modeling (SEM), both in and outside of R. In the R world, the three most popular are lavaan, OpenMX, and sem. Value. 7m. warning(" lavaan WARNING: some estimated lv variances are negative ")} # 2. is cov.lv (PSI) positive definite? Recall that variances involve the sum of squares, which can never be negative. vcov(first.fit) contains the sampling (co)variances of estimated parameters (not variables). When variables in your model have very different variances it can cause some issues in estimation. warning(" lavaan WARNING: some estimated ov variances are negative ")} # 1b. This may be a symptom that the model is not identified. Joreskog, K.G. Large negative residual variance (like in case of e45) can be a sign that your model is not appropriate for your data and needs to be changed. In this case the model implied variances for your three variables are: 0.38, 2.44, and 2.56. Though they have several potential causes, structural misspecification is among the most important. We know the values of x1 x 1 and x2 x 2 and the correlation between them. 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.

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