Trying to fit a Latent Growth Model in R and I get the error " Could not compute standard errors! The information matrix could not be inverted." I know that there are several other posts dealing with this same issue but I still can't seem to figure out why my model isn't working. I don't believe it is under identified because it is a fairly simple model with only a few parameters to estimate. Also I have run this in Lisrel using Simplis and it does work. I get the same estimates when I run it using the sem() function using Lavaan, but it doesn't provide standard errors. Here is my code, which should be pretty straightforward:
Model = "
    # Latent Variables
        Intercept =~ 1*Health1 + 1*Health2 + 1*Health3 + 1*Health4
        Slope =~ 0*Health1 + 1*Health2 + 2*Health3 + 3*Health4

    # Defining Intercepts
        Intercept ~ 1
        Slope ~ 1
"


lower <- '
2.021
1.234 1.892
1.432 1.321 1.705
1.194 1.298 1.321 1.621'

Covariance <-
  getCov(lower, names = c("Health1", "Health2", "Health3", "Health4"))

Sample.Size = 562

Sample.Means = c(3.301, 3.642, 3.998, 4.420)

Fit2 = sem(model = Model, 
          sample.cov = Covariance, 
          sample.nobs = Sample.Size,
          sample.mean = Sample.Means)

summary(Fit2, fit = T)


Warning message:
In lav_model_vcov(lavmodel = lavmodel, 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.


You're trying to specify a growth-model. lavaan offers the growth function. Note that sem and growth are wrapper functions and the lavaan function has a lot of options that need to be considered. From the lavaan manual regarding growth
The growth function is a wrapper for the more general lavaan function, using the 
following default arguments: meanstructure = TRUE, int.ov.free = FALSE, 
int.lv.free = TRUE, auto.fix.first = TRUE (unless std.lv = TRUE), 
auto.fix.single = TRUE, auto.var = TRUE, auto.cov.lv.x = TRUE, 
auto.efa = TRUE, auto.th = TRUE, auto.delta = TRUE, and auto.cov.y = TRUE.

(refer to the manual page regarding lavOptions for the effect of each of these arguments). sem sets these arguments differently. Thereby
Fit2 = growth(model = Model, 
       sample.cov = Covariance, 
       sample.nobs = Sample.Size,
       sample.mean = Sample.Means)

summary(Fit2, fit = T)

gives the required output.
