# Choice of path weights in SEM conceptual models for identical & fraternal twins using openMx

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 bmi1 and bmi2 (of strictly monozygotic twin pairs). I have two questions:

1. 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?

2. Why is this value estimated strictly from the monozygotic twins?

2) The value 0.6 is the starting value not for the intercept of T1 and T2 (path between "one" and T1 & T2), but it is instead the starting value for the factor loadings linking each latent variable (A, C, E) to their indicator T1 or T2. This is indicated by the fact that the path goes from=c("A1","C1","E1"), to="bmi1" in the first case, and from=c("A2","C2","E2"), to="bmi2" in the second case.
As a general note, I will point out that the choice of starting values for any parameter estimate becomes VERY important if the argument free is set to FALSE, because the starting value will effectively become the value of the parameter estimate in the final model (it will not be estimated; it is fixed before estimation).