when I run it, it says: "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."
Y1~iy1(0.05)*1 #intercept for y1Y2~iy2(0.17)*1 #intercept for y2
Thank you very much for your help. I realised my mistake and that I should not have assigned and labelled those intercepts. Even without the assigned values to the intercepts, the model ran with a warning that the information matrix could not be inverted. I now understand from your answer that it is because they are ordered variables, converted to latent responses by lavaan, and thus assigned fixed intercepts and variances.
The model runs if I no longer freely estimate the intercepts and variances of the ordered variables, so thank you!
As a follow-up question, when examining parameterTable for the model that ran successfully, I noticed the following parameters: Y1 ~*~ Y1 and Y2 ~*~ Y2 (both fixed as 1). I read that this represents scaling the factors in the Delta paramaterisation. Is this what you meant when you said lavaan assigns fixed intercepts and variances to the latent responses?
Is this what you meant when you said lavaan assigns fixed intercepts and variances to the latent responses?