Please help me. Can I use a vcv matrix of factor scores as input to lavaan?

[Christopher Galgo]

I have a covariance matrix from factor scores and I want to use that as input for my analysis. 

Firstly, I tested my model in lavaan and I received a lavaan warning saying : some esimated ov variances are negative / covariance matrix of latent variables is not positive definite; so what I did is I tried to do teh steps outlined in Roseel on Factor Score Regression. I did factor score analysis first and produced a vcv matrix out of the factor score loadings. To be honest after this I just dont know how to proceed in R and I do not know if this will produce anything but I hope someone can help me how to move forward. I was wondering if anyone can help me if this still produce nothing on how does one do Croon's correction in R? I have limited statistical background and I am trying to understand what they mean by "TRUE" latent variables? 

Moreover, I was wondering if not having the same amount of factor affects anything in my model? Thank you. 

So this was the covariance matrix I produced. 

              bc1                        bc2                 dv1               dv2              fc1                  fc2.     

bc1    0.887552662 0.004782191  0.06123076  0.02554806 0.218898581  0.13123927

bc2    0.004782191 0.994807132  0.28404452  0.08107698 0.162241929  0.28466757

dv1    0.061230755 0.284044520  0.77432820  0.14407436 0.232051684  0.07219528

dv2    0.025548063 0.081076979  0.14407436  0.62335210 0.013623666  0.20646870

fc1    0.218898581 0.162241929  0.23205168  0.01362367 0.756387914  0.04228952

fc2    0.131239270 0.284667570  0.07219528  0.20646870 0.042289517  0.98754576

info1  0.081799350 0.358589395  0.30719286  0.35342787 0.043581747  0.34527086

sc1    0.143758352 0.114136844  0.14158094 -0.05666471 0.263336420 -0.12509480

so1    0.002854240 0.289255740  0.33211637  0.20766151 0.003479363  0.28322543

vs1    0.234104713 0.086719244 -0.04805958 -0.03642953 0.041590666  0.09575327

vs2   -0.071045262 0.324794629  0.30728513  0.19251651 0.119356966  0.23614446

vs3    0.309499653 0.176341682 -0.01385311  0.20985285 0.135037334  0.16708045

             info1                    sc1              so1                   vs1                    vs2                vs3

bc1    0.08179935  0.14375835  0.002854240  0.2341047127 -0.0710452620  0.309499653

bc2    0.35858939  0.11413684  0.289255740  0.0867192443  0.3247946289  0.176341682

dv1    0.30719286  0.14158094  0.332116373 -0.0480595837  0.3072851332 -0.013853110

dv2    0.35342787 -0.05666471  0.207661513 -0.0364295293  0.1925165138  0.209852848

fc1    0.04358175  0.26333642  0.003479363  0.0415906660  0.1193569658  0.135037334

fc2    0.34527086 -0.12509480  0.283225433  0.0957532652  0.2361444610  0.167080450

info1  0.91411023 -0.09915263  0.496686186  0.0509772815  0.2853273889  0.073079203

sc1   -0.09915263  0.99501013 -0.029381342  0.2296454833  0.0375761612  0.048804598

so1    0.49668619 -0.02938134  0.804089874 -0.0402545871  0.1527907735  0.140742117

vs1    0.05097728  0.22964548 -0.040254587  0.9641480486 -0.0001142417  0.011572463

vs2    0.28532739  0.03757616  0.152790774 -0.0001142417  0.9950109643 -0.001316469

vs3    0.07307920  0.04880460  0.140742117  0.0115724628 -0.0013164690  0.793047306