# Solved – SEM model in lavaan: Can’t compute standard errors

I was playing around in lavaan to bind together two simple models I previously tested on their own via simple regression analyses. I followed the tutorial provided on the following site: http://lavaan.ugent.be/tutorial/sem.html.

Basically, I have two latent variables ($$lv1$$ and $$lv2$$) one has three manifest indicators ($$x1$$, $$x2$$, $$x3$$), the other one has four ($$x1$$, $$x2$$, $$x3$$, $$x4$$). Both variables predict another variable $$y$$. Visually speaking:

The data used only contains positive values. I modeled this as following in R and lavaan:

``semModel <- '   # measurement models     lv1 =~ x1 + x2 + x3     lv2 =~ x1 + x2 + x3 + x4   # regressions     y ~ lv1     y ~ lv2   # residual correlations     x1 ~~ x2     x1 ~~ x3     x1 ~~ x4     x2 ~~ x3     x2 ~~ x4     x3 ~~ x4 ' ``

Then I ran the following:

``fit <- sem(semModel1, data = experimentalData) summary(fit) ``

This returned the following errors:

``1: In lav_model_vcov(lavmodel = lavmodel2, lavsamplestats = lavsamplestats,  :   lavaan WARNING:     Could not compute standard errors! The information matrix could     not be inverted. This may be a symptom that the model is not     identified. 2: In lav_object_post_check(object) :   lavaan WARNING: some estimated lv variances are negative ``

I then added the option `std.ov` to standardise observed variables which still yields the error regarding the standard errors.

``fit <- sem(semModel, data = experimentalData, std.ov = TRUE)  In lav_model_vcov(lavmodel = lavmodel2, lavsamplestats = lavsamplestats,  :   lavaan WARNING:     Could not compute standard errors! The information matrix could     not be inverted. This may be a symptom that the model is not     identified. ``

In the second case, output is as following:

``lavaan 0.6-5 ended normally after 32 iterations    Estimator                                         ML   Optimization method                           NLMINB   Number of free parameters                         21    Number of observations                           583  Model Test User Model:    Test statistic                                    NA   Degrees of freedom                                -6   P-value (Unknown)                                 NA  Parameter Estimates:    Information                                 Expected   Information saturated (h1) model          Structured   Standard errors                             Standard  Latent Variables:                    Estimate  Std.Err  z-value  P(>|z|)   lv1 =~                                             x1                1.000                                x2                1.155       NA                       x3                1.691       NA                     lv2 =~                                            x1                1.000                                x2                2.006       NA                       x3                2.224       NA                       x4                1.422       NA                    Regressions:                    Estimate  Std.Err  z-value  P(>|z|)   y ~                                             lv1               0.020       NA                       lv2               0.889       NA                    Covariances:                    Estimate  Std.Err  z-value  P(>|z|)  .x1   ~~                                                .x2                0.033       NA                      .x3                0.134       NA                      .x4               -0.003       NA                    .x2   ~~                                                .x3               -0.104       NA                      .x4               -0.202       NA                    .x3   ~~                                                .x4                0.013       NA                     lv1  ~~                                             lv2              -0.159       NA                    Variances:                    Estimate  Std.Err  z-value  P(>|z|)    .x1                0.918       NA                      .x2                0.415       NA                      .x3                0.444       NA                      .x4                0.405       NA                      .y                 0.772       NA                       lv1               0.105       NA                       lv2               0.294       NA                   ``

Where did I miss something? Are there (logical) errors in the definition of my model?

Contents