Dear Mike,

I am currently working on a metaSEM study. I've got some error, even I tried to use different methods (see below!). I also asked for a pattern of NA. I would like to ask if, in your opinion, the error messages are related to missing correlations? I have 379 lines, but only 1 or 2 correlations for some variables. Is there a way to continue this study? Maybe dropping some variables?

All the best,

Robert

sstage1fixed <- tssem1(Cov=cordat, n=data$N, method="FEM")

Error in if (!all(isPD)) warning(paste("Group ", (1:no.groups)[!isPD], :

missing value where TRUE/FALSE needed

stage1fixed <- tssem1(Cov=cordat, n=data$N, method="REM")

Error in solve.default(t(X) %*% V_inv %*% X) :

Lapack routine dgesv: system is exactly singular: U[1,1] = 0

> pattern.na(cordat,show.na=FALSE)

v1 v2 v3 v4 v5 v6

v1 1 10 2 8 19 7

v2 10 0 25 30 210 16

v3 2 25 3 7 68 7

v4 8 30 7 4 40 18

v5 19 210 68 40 3 17

v6 7 16 7 18 17 341

Hi Robert,

Could you please post the code and data? Thanks.

Mike

Hi Mike,

I've attached the R code and the data file. I am eager to find out your opinion regarding my data. Is it possible to extract and use a summary matrix?

Robert

Hi Robert,

You may try the following approaches:

Mike

Dear Mike,

This is a great help. Thank you so much!

Best wishes

Robert

Dear Mike,

With your help, I managed to aggregate the correlations in the first stage of a random effect TSSEM metaSEM analysis. While trying to estimate the fit the model-data consistency, I encountered two error messages:

1: In .solve(x = object$mx.fit@output$calculatedHessian, parameters = my.name) :

Error in solving the Hessian matrix. Generalized inverse is used. The standard errors may not be trustworthy.

2: In checkRAM(Amatrix = Amatrix, Smatrix = Smatrix, cor.analysis = cor.analysis) :

The variances of the independent variables in 'Smatrix' must be fixed at 1.

I have attached the entire Rcode, the obtained output and data files. I would appreciate your help, time and consideration!

Regards,

Robert

Hi Robert,

It seems that you are using an old version of metaSEM. Could you please update it and rerun it again?

When you post the R code, could you also remove the ">" and "+" symbols so that we can run the code? Thanks.

Best,

Mike

Hi Mike,

Thank you for your suggestion, I have updated my metaSEM and now I have an output. I encountered some difficulties in running stage1random <- rerun(stage1random, autofixtau2 = TRUE) code, several times appeared a warning message " Not all eigenvalues of Hessian are greater than 0:" (see the attached output). Finally, after specifying the model I managed to estimate the parameters of the model, but I have some concerns about the last warning message I got regarding OpenMx status1 (see below). Sorry for the inconvenient format of the sent code, now I have removed all the mentioned elements and it can be run.

You already helped me a lot, many thanks!

Best,

Robert

"OpenMx status1: 6 ("0" or "1": The optimization is considered fine.

Other values indicate problems.)

Hi Robert,

The stage 2 analysis seems to work better without the diagonal constraints, i.e., diag.constraints=FALSE.

It works fine for me.

Best,

Mike

Dear Mike,

I have to thank you once again for your time and priceless help. However, I would like to ask two more questions that would help me to complete this project.

The first question concerns the possibility of using metaSEM for exploratory purposes, more precisely, is there a possibility to calculate modification indices in metaSEM?

The second question is about non-independent correlations. I read a lot about how to handle such dependencies in MASEM, but I am not sure how should I handle them in metaSEM? Can you help me with some advice in this regard? Perhaps, should we use the same strategy when we have multiple, correlated dependent variables or multiple, correlated independent variables?

Best,

Robert

Dear Robert,

For the first question, you may try the

`mxMI()`

in`OpenMx`

. As I haven't tried it before, I don't know how good it is. Using the`Digman97`

example in the`metaSEM`

package, you may try:Regarding the second question, I don't have a good answer yet. There are different types of dependence. For example, repeated measures and nested samples.

Best,

Mike

Dear Mike,

sorry for the delayed response, but I was out of the office for a while. Regarding dependence, essentially I am facing to problems. The first is linked to the already discussed project, which includes two types of effect sizes: i) correlation between two or more variables obtained from a single study; ii) correlation between two or more variables estimated repeatedly in different waves of a longitudinal study. In this way, some correlations are related given the same sample.. The second problem with dependent effect sizes is linked to another, ongoing project in which I have to test the effect of three correlated predictors (different facets of the same construct) on a single outcome variable. In this case, I have to handle the correlation between the predictors, and I think it would not be a good idea to run three univariate MA? Should I use MASEM estimating a mean direct/indirect effect simultaneously ignoring the possibility of heterogenous direct/indirect effects? Or should I choose a multivariate or three-level SEM-based MA, trying to take into account and model such effects? I feel already deeply indebt, even for the opportunity to discuss methodological problems. Sincerely, Robert

You may use whatever to handle the dependent correlation matrices. This includes, e.g., a multivariate, three-level meta-analyses or robust standard errors. The average correlation matrix with its asymptotic covariance matrix can be fitted with the wls() function.