Attachment | Size |
---|---|
Result of meta-SEM [6] | 635.94 KB |
Result of is.pd [7] | 125.26 KB |
R-code for meta-SEM.R [8] | 5.02 KB |
Arbour2010.csv [9] | 96 bytes |
Ellis2009.csv [10] | 115 bytes |
godin1986.csv [11] | 96 bytes |
Haegele2017.csv [12] | 86 bytes |
Kosma2007.csv [13] | 113 bytes |
Kosma2009hr.csv [14] | 96 bytes |
Kosma2009MET.csv [15] | 95 bytes |
latimer2004paraintense.csv [16] | 111 bytes |
latimer2004paramild.csv [17] | 116 bytes |
latimer2004paramoderate.csv [18] | 110 bytes |
latimer2004tetraintense.csv [19] | 107 bytes |
latimer2004tetramild.csv [20] | 107 bytes |
latimer2004tetramoderate.csv [21] | 111 bytes |
latimer2005.csv [22] | 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?