fvbmtests: Hypothesis testing for a fully-visible Boltzmann machine.
In BoltzMM: Boltzmann Machines with MM Algorithms

Description Usage Arguments Value Author(s) References Examples

Tests the hypothesis that the true bias and interaction parameter values are those in nullmodel, given data and model.

1	fvbmtests(data, model, nullmodel)

`data`	An N by n matrix, where each of the N rows contains a length n string of spin variables (i.e. each element is -1 or 1).
`model`	List generated from `fitfvbm`.
`nullmodel`	A list containing two elements: a vector of length n `bvec`, and an n by n matrix `Mmat`. A list generated by `fitfvbm` is also sufficient.

A list containing 4 objects: a vector containing the z-scores corresponding to the bias parameters bvec_z,a vector containing the p-values corresponding to the bias parameters bvec_p,a matrix containing the z-scores corresponding to the interaction parameters Mmat_z, and a matrix containing the standard errors corresponding to the interaction parameters Mmat_p.

Andrew T. Jones and Hien D. Nguyen

H.D. Nguyen and I.A. Wood (2016), Asymptotic normality of the maximum pseudolikelihood estimator for fully-visible Boltzmann machines, IEEE Transactions on Neural Networks and Learning Systems, vol. 27, pp. 897-902.

# Generate num=1000 random strings of n=3 binary spin variables under bvec and Mmat.
num <- 1000; bvec <- c(0,0.5,0.25); Mmat <- matrix(0.1,3,3) - diag(0.1,3,3);
data <- rfvbm(num,bvec,Mmat)
# Fit a fully visible Boltzmann machine to data, starting from parameters bvec and Mmat.
model <- fitfvbm(data,bvec,Mmat)

#Propose a null hypothesis model
nullmodel <- list(bvec = c(0,0,0), Mmat = matrix(0,3,3))

# Compute z-scores
fvbmtests(data,model,nullmodel)