getModelFitness: Scoring the given SEM models.
In stablespec: Stable Specification Search in Structural Equation Models

Description Usage Arguments Value Author(s) Examples

Compute the model chi-square and model complexity of the given SEM models.

1 2	getModelFitness(theData = NULL, allModelString = NULL, numTime = NULL, longitudinal = NULL, co = NULL, mixture = NULL)

`theData`	a data frame containing the data to which the model is to be fit. If parameter `longitudinal` is `TRUE`, the data frame should be reshaped such that the first `n` data points contain the relations that occur in the first two time slices `t_0` and `t_1`. The next `n` data points contain the relations that occur in time slices `t_1` and `t_2`. The `i-th` subset of `n` data points contain the relations in time slices `t_i-1` and `t_i`. One can use function `dataReshape` to reshape longitudinal data.
`allModelString`	`m` by `n` `matrix` of binary vectors representing models, where `m` is the number of models, and `n` is the length of the binary vector.
`numTime`	number of time slices. If the data is cross-sectional, this argument must be set to 1.
`longitudinal`	`TRUE` for longitudinal data, and `FALSE` for cross-sectional data.
`co`	whether to use `"covariance"` or `"correlation"` `matrix`.
`mixture`	if the data contains both continuous and categorical (or ordinal) variables, this argument can be set to `TRUE`. This implies the use of `polychoric` and `polyserial` correlation in the SEM computation. Note that, the categorical variables should be represented as `factor` or `logical`.

a matrix of models including their fitness': chi-square and model complexity.

Ridho Rahmadi r.rahmadi@cs.ru.nl

the_data <- crossdata6V
#assummed that variable 5 does not cause variables 1, 2, and 3
models <- modelPop(nPop=5, numVar=6, longitudinal=FALSE,
consMatrix = matrix(c(5, 1, 5, 2, 5, 3), 3, 2, byrow=TRUE))

model_fitness <- getModelFitness(theData=the_data,
allModelString=models, numTime=1, longitudinal=FALSE,
co="covariance", mixture = FALSE)