BMNExact: Exact inference in L1-penalized Binary Markov Model
In BMN: The pseudo-likelihood method for pairwise binary markov networks

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

View source: R/BMNExact.R

Functions to calculate a parameter estimates for L1-penalized Binary Markov Models.

BMNExact(X, rhoVec, thrCheck=1e-3, thrPseudo=1e-5, ThetaStart=NULL, 
verbose = FALSE, maxIter=100, timeout=60, penalize.diag=FALSE)
BMNExact.single(X, rho, thrCheck=1e-3, thrPseudo=1e-5, ThetaStart=NULL, 
verbose = FALSE, maxIter=100, timeout=60, penalize.diag=FALSE)

`X`	Input data matrix consisting of 0-1 entries. Has n rows and p columns.
`rho`	Value of the penalty parameter; If a non-negative p-by-p matrix is given, it is used as the penalty structure.
`rhoVec`	Gives all values of rho for which the solution should be calculated.
`thrCheck`	Error threshold at which convergence is declared.
`thrPseudo`	Error threshold for the interal pseudolikelihood algorithm.
`ThetaStart`	Starting value for Theta, has to be a p-by-p matrix.
`verbose`	Should status messages be printed.
`maxIter`	Maximum number of iteratios to run.
`timeout`	Number of seconds after which the procedure is stopped; for the path algorithm, this is reset for every value of rho.
`penalize.diag`	Should the diagonal be penalized?

The function BMNExact fits a penalized pairwise binary Markov model to the data provided as matrix X for each of the elements in the penalty parameter vector rhoVec (note that rhoVec will be sorted in increasing order). Internally, the function BMNExact.single is called for each entry in rhoVec and the results are collected as described below.

`rho`	Vector of penalty parameters sorted in increasing order.
`ThetaList`	A list of Theta pxp matrices, corresponding to the penalty parameters in rho.
`success`	A logical vector of the same length as rho. True, if the function succeeded for the corresponding value in rho.
`penalize.diag`	Logical. Indicates if the diagonal was penalized (same as input value `penalize.diag`.

Holger Hoefling

BMNPseudo, BMNJT

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;
numSamples=1000; burnIn=100; skip=1;

simData = BMNSamples(Theta, numSamples, burnIn, skip)

rhoVec = c(0.01, 0.02, 0.03)

exactPath = BMNExact(simData, rhoVec)
exactSingle = BMNExact.single(simData, 0.02)