Double-checking a simple single-latent-variable SEM

[edm]

Instead of restricting the first weight to be positive, it seems to work better if you leave the weight unrestricted in the parameter block, then “fix” the signs in generated quantities. See here:

Latent factor loadings

For a one factor model, I would usually declare the loadings to be unconstrained in the parameters block and in the generated quantities block, multiply stuff by -1 as necessary to force the sign of one loading to be positive. For a multi-factor model with an identity matrix for the correlations among the factors, a cholesky_factor_cov can work well as a loadings matrix since it is lower trapezoidal with positive diagonal entries. But that can run into problems like the one you encountered, if …

Or, if you fixed the first weight to 1, the SD of the latent distribution would then typically be free.