Greetings,
I have run a random-effects TSSEM (tssem2()) in metaSEM and received the estimated path loadings and correlations among factors in the output for my structural model. I also need to find the R2 values for my endogenous variables in my structural model. I was wondering how I can determine R2 values. I think they should be calculated as 1-var of the endogenous variables found in impliedS1 matrix in mx.fit component of the tssem2() output:
random2<-tssem2(...)
random2$mx.fit$impliedS1
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
[1,] 1.00000000 -0.2124499 -0.37657180 -0.1408749 0.1566710 0.03624335 -0.16619094 -0.06419929
[2,] -0.21244990 0.4783484 0.57574642 0.4799511 0.5499434 0.44504316 0.53299814 0.14718394
[3,] -0.37657180 0.5757464 0.77637611 0.5277954 0.6590216 0.60979639 0.56536845 0.04923937
[4,] -0.14087491 0.4799511 0.52779536 0.5113916 0.5535190 0.40219453 0.58033105 0.22417971
[5,] 0.15667096 0.5499434 0.65902158 0.5535190 1.0000000 0.46021062 0.56435678 -0.10231421
[6,] 0.03624335 0.4450432 0.60979639 0.4021945 0.4602106 1.00000000 0.45487242 0.20794375
[7,] -0.16619094 0.5329981 0.56536845 0.5803310 0.5643568 0.45487242 1.00000000 -0.08405294
[8,] -0.06419929 0.1471839 0.04923937 0.2241797 -0.1023142 0.20794375 -0.08405294 1.00000000
Then, for example,
R2 for [,2]= 1- 0.4783484= 0.5216516.
Am I right?
I appreciate any help,
Thank you,
Hamed
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Hi, Hamed.
In your example, 0.4783484 is already the explained variance while 0.5216516 is the error variance.
An alternative approach to calculate the error variance is diag(mxEval(Ematrix, random2$mx.fit)) . Thus, the R squares are 1-diag(mxEval(Ematrix, random2$mx.fit)) .
Cheers,
Mike
Thank you, Mike. Your explanations helped a lot.
Cheers,
/Hamed