CI.test {gRapHD} | R Documentation |
Test of conditional independence.
CI.test(x,y,S,dataset,homog=TRUE)
x |
one of the variables. |
y |
the other variable. |
S |
separator (possibly NULL ). |
dataset |
matrix or data frame (nrow(dataset) observations and
ncol(dataset) variables). |
homog |
TRUE for homogeneous covariance structure, FALSE
for heterogeneous. This is only meaningful with mixed models.
Default is homogeneous (TRUE ). |
Performs a test of conditional independence of x and y given a set of variables S. The variables are specified as
column numbers of the dataset. Under the alternative the variables are assumed to follow an unrestricted
(mixed) graphical model. If x and y are discrete, S must also be discrete.
Note that the model dimension returned by the fit
function assumes that all parameters are estimable, which may not be the case for
high-dimensional sparse data. However, here and in the search functions we use
the adjusted degrees of freedom, which need no such assumptions and are believed to be correct.
A list with the deviance (deviance
) and the adjusted degrees of freedom
(numP
).
Gabriel Coelho Goncalves de Abreu (Gabriel.Abreu@agrsci.dk)
Rodrigo Labouriau (Rodrigo.Labouriau@agrsci.dk)
David Edwards (David.Edwards@agrsci.dk)
Lauritzen, S.L. (1996) Graphical Models, Oxford University Press.
Edwards, D. (2000) Introduction to Graphical Modelling, Springer-Verlag
New York Inc.
data(dsCont) m1 <- minForest(dsCont,homog=TRUE,forbEdges=NULL,stat="BIC") CI.test(20,29,c(9,11),dsCont) #$deviance #[1] 0.7617515263220724 #$numP #[1] 1