BMNJT: Junction tree algorithm for pairwise binary Markov networks

In BMN: The pseudo-likelihood method for pairwise binary markov networks

Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/BMNJT.R

Description

Calculates the expections and second moments for a pairwise binary Markov network using the junction tree algorithm.

Usage

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BMNJT(thetaMat, adjMat = NULL, var = NULL, onlyActive = FALSE, timeout = 60)

Arguments

Details

Uses the junction tree algorithm to calculate the matrix of second moments of the underlying pairwise binary Markov model. This is useful for inference on this type of models as the matrix of second moments is the derivative of the partition function in this class of models. The function provides the option to calculate the second moments only for one variable with all the others (variables are numbered from 1 to p; see var). Also, the juntion tree algorithm is by a factor p more efficient if only second moments corresponding to edges in the underlying graph (non-zero values in the thetaMat or adjMat) are being calculated. This can be done using the switch onlyActive.

Value

The return value is a list with elements:

Author(s)

Holger Hoefling

See Also

BMNPseudo, BMNExact

Examples

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library(BMN)
Theta = matrix(numeric(25), ncol=5)
Theta[1,1]=0.5; Theta[2,2]=0.5; Theta[3,3]=0; Theta[4,4]= -0.5; Theta[5,5]= 0.5;
Theta[1,2]=Theta[2,1]=1; Theta[1,4]=Theta[4,1]=1; Theta[2,3]=Theta[3,2]= -1;

BMNJT(Theta)