metaSEM

Hi Mike,

Thanks very much for your excellent metaSEM package. I have successfully wrangled my data into the necessary format and run a fixed effect model and then a random effects model with the "diag" option. The data is 37 samples (ranging in size from 28 to 1589) of 8 variables (and none of the samples have missing data; all samples provide all 28 correlations). I can't get the random effects model to run with the "Symm" option, and it is driving me nuts as to why this is the case. I get Open Mx error code 6, and all of the parameters come out NA.

Hi metaSEM users, hi Mike,

I am just wondering if and how to apply a moderator analysis in metaSEM. Can I apply the approach you describe in your book beginning on page 170? I would like to test a moderating effect between two effect sizes by treating one variable as moderator. Or is there any other approach?

Thanks for your help.

Johannes

Hi,
I would like to perform a mediation analysis by defining the indirect effects with mx.algebras in the wls() funktion.
I tried mx.algebras= list( ind=mxAlgebra(b410*b14, name="ind"))), where b410 and b14 refer to the paths in the A matrix. However, I get the following error in the analysis:

Error in running the mxModel:

Hi metaSEM users, hi Mike,

I enjoy using the metaSEM package for my analysis in R. I amjust wondering how I can compare path cofficients in an estimated model (t-test) ? Probably it is quite easy, but I am new to R. So help is really appreciated :)

Thanks
Johannes

Hi metaSEM users, hi Mike,

I've been attempting to use indirectEffect() to estimate the direct and indirect effects from a series of 3 by 3 correlation matrices. I then want to meta-analyze the resulting direct and indirect effects using either meta() from the metaSEM package or mvmeta() from the mvmeta package in R.

I am trying to derive a standard error for standardized indirect effects but am unclear how to find their standard errors using the output below. Is there a way to calculate the SE from the information below?

x <- matrix(c(1, -0.07, -0.23, -0.07, 1, 0.35, -0.23, 0.35, 1), ncol=3)
dimnames(x) <- list( c("y", "m", "x"), c("y", "m", "x") )
indirectEffect(x, n=81, standardized = TRUE )