BMNPseudo: Pseudo-likelihood inference in L1-penalized Binary Markov...
In BMN: The pseudo-likelihood method for pairwise binary markov networks

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

View source: R/BMNPseudo.R

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

BMNPseudo(X, rhoVec, Delta=NULL, ThetaStart=NULL, 
maxError=1e-5, verbose=FALSE, maxIter=100, penalize.diag=FALSE, 
stepSize=1, performLineSearch=FALSE)
BMNPseudo.single(X, rho, Delta=NULL, ThetaStart=NULL, 
maxError=1e-5, maxIter=100, penalize.diag=FALSE, 
stepSize=1, performLineSearch=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.
`Delta`	Adjustmeant to the gradient.
`ThetaStart`	Starting value for Theta, has to be a p-by-p matrix.
`maxError`	convergence threshold for the algorithm.
`verbose`	Print status messages.
`maxIter`	Maximum number of iteratios to run.
`penalize.diag`	Should the diagonal be penalized?
`stepSize`	Stepsize of the algorithm; should be 1 or less.
`performLineSearch`	If TRUE, a line search is performed; takes longer but is guarateed to converge.

The function BMNPseudo fits an approximate 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 non-negative penalty parameters sorted in decreasing 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

BMNExact, 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)

pseudoPath = BMNPseudo(simData, rhoVec)
pseudoSingle = BMNPseudo.single(simData, 0.02)