I am reviewing the R package OpenMx for a genetic epidemiology analysis in order to learn how to specify and fit SEM models. I am new to this so bear with me. I am following the example on page 59 of the OpenMx User Guide. Here they draw the following conceptual model:
And in specifying the paths, they set the weight of the latent "one" node to the manifested bmi nodes "T1" and "T2" to be 0.6 because:
The main paths of interest are those from each of the latent variables to the respective observed variable. These are also estimated (thus all are set free), get a start value of 0.6 and appropriate labels.
# path coefficients for twin 1 mxPath( from=c("A1","C1","E1"), to="bmi1", arrows=1, free=TRUE, values=0.6, label=c("a","c","e") ), # path coefficients for twin 2 mxPath( from=c("A2","C2","E2"), to="bmi2", arrows=1, free=TRUE, values=0.6, label=c("a","c","e") ),
The value of 0.6 comes from the estimated covariance of
bmi2 (of strictly monozygotic twin pairs). I have two questions:
When they say that the path is given a "starting" value of 0.6 is this like setting a numerical integration routine with initial values, like in estimation of GLMs?
Why is this value estimated strictly from the monozygotic twins?