Overlap of input matrices (missing variables)

Hello,

I am Valerie, 25 years old and absolutely fascinated by applying Metasem respectively TSSEM by using metaSEM. When I was doing my first analysis, I came across a problem concerning the input data respectively the input correlation matrices and did not find an solution so far:

Let's say, I have two studies, which are dedicated to the same topic, but slightly differ in their observed variables, what is also reflected in their correlation matrices.
1) Study1 shows a correlation matrix with the variables x1,x2,x3
2) Study2 shows a corrleation matrix with the variables x1,x2,x4
This data is different when compared to the datasets such as Hunter83 provided in the book (Cheung 2015), because in this case, there is no correlation matrix, which is "complete", as variables (and as a result also the correlations) are missing in the studies.
Then input matrix would look as follows (taking random values for correlation coefficients):
1
0.2 1
0.3 0.5 1
NA NA NA NA
1
0.7 1
NA NA NA
0.4 0.2 NA 1

When I imported the data using the readLowMat() function, it worked out. However, when applying tssem1(), there is an error message in case of method="FEM" (Error in if (!all(isPD)) warning(paste("Group ", (1:no.groups)[!isPD], :
missing value where TRUE/FALSE needed) and in case of method="REM" (Error in if (all.equal(covMatrix, t(covMatrix))) { :
argument is not interpretable as logical). My guess was that the kind of input data is not suitable for TSSEM as there is not "complete matrix" available, when it comes to the equality constraints. But I do not the correct procedure to apply now. I have read about pairwise or listwise deletion, but isn't this an issue, TSSEM is able to avoid? Or do I mix up the cases of missing variables and missign correlations?

I really would appreciate any help and good advice. I send best wishes from Germany!