stablespec (version 0.1.3)

modelPop: Random SEM models.

Description

Generating recursive (acyclic) SEM models represented by binary vectors. The generated models also include a model from each model complexity.

Usage

modelPop(nPop = NULL, numVar = NULL, longitudinal = NULL, consMatrix = NULL)

Arguments

nPop

number of models to generate or population size.

numVar

number of variables.

longitudinal

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 m matrix, 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, where minPop = numVar(numVar-1)/2+1 if longitudinal = FALSE, or minPop = (numVar(numVar-1)/2+1)+numVar^2 if longitudinal = TRUE. If nPop <= minpop<="" code=""> then this function will generate minPop models.

Examples

models <- modelPop(nPop=25, numVar=6,
longitudinal=FALSE, consMatrix = matrix(c(1, 2), 1, 2))
models