lme4ord for statisticians

Call the lme4 machinery more directly, in a manner analogous to lm.fit

require(Matrix) # sparse matrices are required!
devFun <- mkGeneralGlmerDevfun(y = responseVector,
                               X = modelMatrixFixef, 
                               Zt = as(t(modelMatrixRanef), "dgCMatrix"),
                               Lambdat = as(upperTriangularCovarFactor, "dgCMatrix"),
                               family = binomial(),
                               initPars = initialParameterVector,
                               parInds = listOfIndicesToParameterVector,
                               mapToCovFact = function(covarianceParameters) ..., 
                               ...)

Now optimize it!

opt <- optim(initialParameterVector, devFun)

The fitted objects are in the environment of the deviance function

environment(devFun)$pp$beta(1)

source »