Attachment | Size |
---|---|
Result of meta-SEM | 635.94 KB |
Result of is.pd | 125.26 KB |
R-code for meta-SEM.R | 5.02 KB |
Arbour2010.csv | 96 bytes |
Ellis2009.csv | 115 bytes |
godin1986.csv | 96 bytes |
Haegele2017.csv | 86 bytes |
Kosma2007.csv | 113 bytes |
Kosma2009hr.csv | 96 bytes |
Kosma2009MET.csv | 95 bytes |
latimer2004paraintense.csv | 111 bytes |
latimer2004paramild.csv | 116 bytes |
latimer2004paramoderate.csv | 110 bytes |
latimer2004tetraintense.csv | 107 bytes |
latimer2004tetramild.csv | 107 bytes |
latimer2004tetramoderate.csv | 111 bytes |
latimer2005.csv | 115 bytes |
First of all, I am grateful for asking my question to this forum where gurus of meta-SEM are helping people like me.
I am currently conducting meta-SME with the theory of planned behavior by following the example of Scalco17 in this website.
https://cran.r-project.org/web/packages/metaSEM/vignettes/Examples.html#scalco17
It seems my dataset work pretty well, but something is messing up the analysis.
Some data show 'NA' when I conducted the test for positive definite matrix using 'is.pd', and I think it is the reason for unreliable result because all results seems good when i conduct the analysis after eliminating data with NA from is.pd.
Would someone help me to figure this issue out?
How can I deal with missing data or NA from is.pd like this?
Could you please include the data and R code so that we can replicate the errors?
Dr. Chueng,
I uploaded R code and data set I used.
The error is likely related to the fact that two of the variances (Tau2_5 and Tau2_9) are near zero.
Rerunning the model seems to work.
I really appreciate your quick and helpful answer!
So, in this case, should I report the result as is?
Since the summary of mx.fit0 shows below message,
** Information matrix is not positive definite (not at a candidate optimum),
does it mean the overall fit of the model is not trustworthy and the result of meta-analysis is not credible?
Does rerunning the model work? It worked fine in my R.
If it still does not work, you may increase the number of trials, e.g., 100, and see if it works better.
Dear Dr. Cheung,
Sorry for late reply.
It worked well when I tried it again with your code.
I really appreciate your kind and helpful answer:)