# 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?