TRUE for longitudinal data,
and FALSE for cross-sectional data.
consMatrix
m by 2 binary matrix
representing constraint/prior knowledge,
where m is the number of constraint. For example, known that
variables 2 and 3 do not cause variable 1, then
constraint <- matrix(c(2, 1, 3, 1), 2, 2, byrow=TRUE)) will be
the constraint matrix.
Value
nPop or minPop by mmatrix,
where m is the length of the binary vector depending
of the given number of variables
and also whether longitudinal or cross-sectional model.
Details
This function generates nPop random SEM models which are
represented by binary vectors; 1 means there is a causal path from,
e.g., variable A to B
and 0 otherwise. In addition, the generated models
have passed the cyclic test to ensure they are all acyclic. The function
also includes minPop models which representing models
from each model complexity, i.e., minPop = numVar(numVar-1)/2+1,
if longitudinal = FALSE, or
minPop = (numVar(numVar-1)/2+1)+numVar^2, otherwise.
If nPop <= minpop<="" code=""> then
this function will generate minPop models.
#assumming a prior knowledge that variable 1 does not cause variable 2models <- modelPop(nPop=25, numVar=6,
longitudinal=FALSE, consMatrix = matrix(c(1, 2), 1, 2))
models