A list of parameters controlling the HLfit fitting algorithm (potentially called by all fitting functions in spaMM), which should mostly be ignored in routine use. Possible controls are:
algebra, sparse_precision: see algebra;
conv.threshold and spaMM_tol: spaMM_tol is a list of tolerance values, with elements Xtol_rel and Xtol_abs that define thresholds for relative and absolute changes in parameter values in iterative algorithms (used in tests of the form “d(param)< Xtol_rel * param + Xtol_abs”, so that Xtol_abs is operative only for small parameter values). conv.threshold is the older way to control Xtol_rel. Default values are given by spaMM.getOption("spaMM_tol");
break_conv_logL: a boolean specifying whether the iterative algorithm should terminate when log-likelihood appears to have converged (roughly, when its relative variation over on iteration is lower than 1e-8). Default is FALSE (convergence is then assessed on the parameter estimates rather than on log-likelihood).
iter.mean.dispFix: the number of iterations of the iterative algorithm for coefficients of the linear predictor,
if no dispersion parameters are estimated by the iterative algorithm. Defaults to 200 except for Gamma(log)-family models;
iter.mean.dispVar: the number of iterations of the iterative algorithm for coefficients of the linear predictor,
if some dispersion parameter(s) is estimated by the iterative algorithm. Defaults to 50 except for Gamma(log)-family models;
max.iter: the number of iterations of the iterative algorithm for joint estimation of dispersion parameters and
of coefficients of the linear predictor. Defaults to 200. This is typically much more than necessary,
unless there is little information to separately estimate \(\lambda\) and \(\phi\) parameters;
resid.family: was a previously documented control (before version 2.6.40), and will still operate as previously documented, but should not be used in new code.
# (., control.HLfit=list(...))