Dissecting lme4's lmer function. Part 3.

This is the final part of my analysis of the function lmer, which is used to fit linear mixed models in the R package lme4. In two previous blog posts, we have seen the general layout of the function lmer, the dealings with the R model formula, and the setting up of the objective function for the optimization (see part 1 and part 2).

After the user-specified R model formula is evaluated to model matrices, vectors and parameters, and the objective function is generated, the function optimizeLmer is called from within lmer to carry out the optimization. We analyse optimizeLmer below.

Minimizing the deviance - optimizeLmer

The function takes as input arguments the previously generated deviance function devfun, the provided (or previously computed by mkLmerDevfun) starting values start for the optimization, and other optimization parameters (such as the method to be used) bundled in the merControl object control.

Then an environment is defined as rho <- environment(devfun). That is, rho contains all the parameters defined by mkLmerDevfun during the generation of devfun; and additionally, the parent environment of rho is the environment from which mkLmerDevfun was called (so, there is access to variables from there as well).

Eventually the function optwrap (which is defined in lmer.R) is called to carry out the actual optimization. We dissect optwrap below. The returned object is saved as opt.

optwrap

  • Use getOptfun in order to check that the user-specified optimizer is supported.

  • Deal with the peculiarities regarding the input arguments of the supported optimizer functions (e.g. modify control so that the verbose argument will be passed on correctly), and set up other input arguments for the optimizer with arglist <- list(fn = fn, par = par, lower = lower, upper = upper, control = control).

  • Call the optimizer:

opt <- withCallingHandlers(do.call(optfun, arglist),
                           warning = function(w) {
                               curWarnings <<- append(curWarnings,list(w$message))
                           })
  • Do some post optimization tweaking: rename the parameters in opt in a consistent way and pass on all warnings.

  • Compute the gradient for the objective function at the estimated minimal values using the function deriv12, which uses a central finite difference method.

  • Store all auxiliary information and return opt:

attr(opt,"optimizer") <- optimizer
attr(opt,"control") <- control
attr(opt,"warnings") <- curWarnings
attr(opt,"derivs") <- derivs
opt

Extended convergence checking - checkConv

If the optimization yields a result, then it is checked against additional convergence criteria by the function checkConv.

  • Compute a scaled gradient as the solution to the linear system with the Cholesky factor of the Hessian as the matrix on the left hand side, and the gradient on the right hand side:
scgrad <- tryCatch(with(derivs,solve(chol(Hessian),gradient)),
                        error=function(e)e)
  • Find the parallel minimum of the gradient and the scaled gradient of the objective function as mingrad <- pmin(abs(scgrad),abs(derivs$gradient)). Check whether the maximal entry of mingrad is above a specified threshold (default is 2e-3).

  • Similarly check the relative gradient against a specified relative tolerance (disabled by default).

  • Check whether the variance of any random effect is below a specified tolerance (i.e. equal to 0), that is, whether we have a singular fit. The default tolerance level here is 1e-4.

  • Check the Hessian of the objective function for convergence. This check is implemented in the function checkHess, which performs the following steps:

    • Check that the Hessian has no negative eigenvalues (less that -tol, where tol is 1e-6 by default).

    • Check that the Hessian does not have very large eigenvalues, determined by $\rho(H) \cdot \mathrm{tol} > 1$ (where $\rho(H)$ is the spectral radius of the Hessian, and tol is 1e-6 by default).

    • Check that the ratio of the minimal to the maximal eigenvalues is not below tol; which is equivalent to the conditional number of the Hessian being smaller than 1/tol.

  • Return all messages and warnings.

Prepare an output object - mkMerMod

This function takes as inputs the environment of the objective function, the parameter estimates obtained from the optimization, the fixed effects and random effects model matrices etc., the original function call, and the messages generated from the convergence check in checkConv. It checks, reorganizes and renames the parameters, and finally returns everything in an object of class lmerMod:

new(switch(rcl, lmerResp="lmerMod", glmResp="glmerMod", nlsResp="nlmerMod"),
    call=mc, frame=fr, flist=reTrms$flist, cnms=reTrms$cnms,
    Gp=reTrms$Gp, theta=pp$theta, beta=beta,
    u=if (trivial.y) rep(NA_real_,nrow(pp$Zt)) else pp$u(fac),
    lower=reTrms$lower, devcomp=list(cmp=cmp, dims=dims),
    pp=pp, resp=resp,
    optinfo = list (optimizer= attr(opt,"optimizer"),
                    control	 = attr(opt,"control"),
                    derivs	 = attr(opt,"derivs"),
                    conv  = list(opt=opt$conv, lme4=lme4conv),
                    feval = if (is.null(opt$feval)) NA else opt$feval,
                    warnings = attr(opt,"warnings"), val = opt$par)
    )
Written on May 17, 2015
Tags: #lme4