Allow the user to set and examine a variety of options which affect operations of the spaMM package.
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a character string holding an option name.
Boolean: whether to warn if a previously undefined options is being defined (a protection against typos).
A named value or a list of named values. The following values, with their defaults,
are used in
and many other undocumented values for programming or development purposes. Additional options without default values can also be used (e.g., see
spaMM.options() provides an interface for changing maximal values of parameters of the Matérn correlation function. However, it is not recommended to change these values unless a spaMM message specifically suggests so.
By default spaMM use Iteratively Reweighted Least Squares (IRLS) methods to estimate fixed-effect parameters (jointly with predictions of random effects). However, a Levenberg-Marquardt algorithm, as described by Nocedal & Wright (1999, p. 266), is also implemented. The Levenberg-Marquardt algorithm is designed to optimize a single objective function with respect to all its parameters. It is thus well suited to compute a PQL fit, which is based on maximization of a single function, the h-likelihood. By contrast, in a fit of a mixed model by (RE)ML, one computes jointly fixed-effect estimates that maximizes marginal likelihood, and random-effect values that maximize h-likelihood given the fixed-effect estimates. The gradient of marginal likelihood with respect to fixed-effect coefficients does not generally vanishes at the solution (although it remains close to zero except in “difficult” cases with typically little information in the data). The Levenberg-Marquardt algorithm is not directly applicable in this case, as it may produce random-effect values that increases marginal likelihood rather than h-likelihood. The (RE)ML variant of the algorithm implemented in spaMM may therefore use additional nested h-likelihood-maximizing steps for correcting random-effect values. In version 3.1.0 this variant was revised for improved performance in difficult cases.
spaMM.getOption, the current value set for option
NULL if the option is unset.
spaMM.options(), a list of all set options. For
spaMM.options(<name>), a list of length one containing the set value,
NULL if it is unset. For uses setting one or more options,
a list with the previous values of the options changed (returned
Jorge Nocedal and Stephen J. Wright (1999) Numerical Optimization. Springer-Verlag, New York.
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spaMM (version 2.7.1) is loaded. Type 'help(spaMM)' for a short introduction, and news(package='spaMM') for news. $use_G_dG  TRUE $TRY_update  TRUE $perm_G  TRUE $TRY_ZAX NULL $sparsity_threshold  0.05 $separation_max  1000 $spprec_method  "def_AUGI0_ZX_sparsePrecision" $matrix_method  "def_sXaug_EigenDense_QRP_Chol_scaled" $Matrix_method  "def_sXaug_Matrix_QRP_CHM_scaled" $EigenDense_QRP_method  ".lmwithQR" $use_spprec_QR  FALSE $LevenbergM NULL $spprec_LevM_D  "1" $mat_sqrt_fn  "mat_sqrt" $USEEIGEN  TRUE $lev_by_sparse_Q  20000 $X_scaling  TRUE $maxLambda  1e+10 $regul_lev_lambda  1e-08 $allow_augZXy NULL $augZXy_solver  "chol" "EigenQR" $augZXy_fitfn  ".HLfit_body_augZXy" $check_alt_augZXy  FALSE $covEstmethod  ".makeCovEst1" $rC_unbounded  FALSE $tol_ranCoefs lo_lam up_lam corr tol 1e-06 1e+06 1e-12 1e-05 $tol_rel_ranCoefs lo_lam up_lam corr 1e-04 1e+05 1e-04 $max_corr_ranCoefs  0.99999 $regul_ranCoefs  1e-09 $condnum_for_latentL  1e+100 $condnum_for_latentL_spprec  1e+11 $condnum_for_latentL_inner  1e+12 $invL_threshold  1e+06 $use_tri_for_augZXy  FALSE $use_tri_for_makeCovEst  TRUE $example_maxtime  0.7 $COMP_maxn  10000 $QRmethod NULL $spaMM_tol $spaMM_tol$Xtol_rel  1e-05 $spaMM_tol$Xtol_abs  1e-06 $spaMM_tol$Ftol_LM  0.1 $optimizer1D  "optimize" $optimizer  "nloptr" $Gamma_min_y  1e-10 $optimize_tol  0.0001220703 $bobyqa list() $nloptr $nloptr$algorithm  "NLOPT_LN_BOBYQA" $nloptr$xtol_rel  5e-06 $nloptr$print_level  0 $maxeval 10^(3 + (log(length(initvec)) - log(5))/log(4)) $xtol_abs .xtol_abs_fn(LowUp) $CMP_asympto_cond (pow_lam_nu > 10/nu) || 1 + pow_lam_nu + 6 * sqrt(pow_lam_nu/(nu)) > .spaMM.data$options$COMP_maxn $rankMethod  "qr" $rankTolerance max(1e-07, .Machine$double.eps * 10 * ncol(X.pv)) $qrTolerance  1e-10 $Zcolsbyrows  FALSE $levels_type  "mf"  0.7
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