Control settings for the IRMC estimator — adirmcControl • admixr2

Constructs a control object for est = "adirmc", the Iterative Reweighting Monte Carlo estimator.

Usage

adirmcControl(
  studies = list(),
  n_sim = 2500L,
  outer_iter = 50L,
  sampling = c("sobol", "halton", "torus", "lhs", "rnorm"),
  algorithm = NULL,
  maxeval = 5000L,
  ftol_rel = .Machine$double.eps,
  print = 1L,
  omega_expansion = 1,
  seed = 12345L,
  cores = rxode2::rxCores(),
  nDisplayProgress = .Machine$integer.max,
  grad = c("analytical", "none", "fd"),
  kappa_method = c("exact", "linearized", "linearized_gh"),
  kappa_n_nodes = 5L,
  grad_h = 1e-06,
  cov_h = 0.001,
  cov_h_outer = .Machine$double.eps^(1/5),
  phases = c(2, 1, 0.5, 0.01),
  convcrit = 1e-05,
  max_worse = 5L,
  covMethod = c("r,s", "r", "none"),
  cov_n_sim = 10000L,
  n_restarts = 1L,
  restart_sd = 0.2,
  workers = 1L,
  rxControl = NULL,
  calcTables = FALSE,
  compress = TRUE,
  ci = 0.95,
  sigdig = NULL,
  sigdigTable = NULL,
  addProp = c("combined2", "combined1"),
  optExpression = TRUE,
  sumProd = FALSE,
  literalFix = TRUE,
  returnAdmr = FALSE,
  resid_nodes = 81L,
  xtol_rel = .Machine$double.eps^(1/2),
  ...
)

Arguments

studies

Named list of study specifications. Each element is a list with:

E – observed mean vector
V – observed covariance matrix or variance vector (auto-detected)
n – sample size
times – numeric vector of observation times
ev – rxode2::et() dosing event table
method – "cov" or "var" (optional; auto-detected from V)
v_denom – "ml" (default) or "unbiased", declaring which denominator the supplied V uses. The likelihood is the exact one for n iid draws only under the ML (n) covariance, which is what cov.wt(method = "ML") and datagen() produce. A published SD is the unbiased (n - 1) SD, so a digitised figure gives V = SD^2 on the n - 1 scale: declare v_denom = "unbiased" and admixr2 converts it. Declared per study, since a meta-analysis routinely mixes a digitised source with a model-derived one and the two need not share a denominator. At n = 60 the factor is 1.7%; it matters more the smaller n is, and more again for any method that scores the reported covariance against its own sampling law.

Multi-compartment (multiple observed outputs). To fit several observed compartments simultaneously (e.g. plasma and brain/CSF), give the study an observations list instead of top-level E/V/times. Each entry is one observed output with its own output (the model prediction variable, e.g. "cp" or "cCSF"), times, E, V and – for independent fits – ev and n. Pass the endpoint names to admData(), e.g. admData(c("cp", "cCSF")), so nlmixr2 recognises every endpoint. There are two modes:

Independent – each observed output has its own n/ev (separate experiments / subjects, e.g. a plasma study and a brain study combined for meta-analysis). The outputs are independent likelihood blocks and the aggregate -2LL is their sum.
Joint (same subjects) – the outputs are measured on the SAME subjects. Give the study a shared n and ev, and a joint covariance either as a study-level full matrix V (blocks in observations order) or as per-output marginal V plus a cross list of cross-covariance blocks keyed "outA:outB" (each length(times_A) x length(times_B); omitted pairs are zero). The compartments are then scored by a single MVN over the stacked vector with shared random effects. est = "adirmc" does not support multiple observed outputs; use "admc", "adfo" or "adgh".

Long format (one row per endpoint/time). As an alternative to the observations list, a study may carry a data frame that keys each observed summary by endpoint, the way nlmixr2 keys observations by DVID/CMT. The frame needs an endpoint column (DVID, CMT or output), a time column (TIME), a mean column (E) and – unless a joint V is given – a variance column (V) or an SD column (SD). It is normalised into exactly the same units as the observations form, so the two are interchangeable:

# independent blocks: per-row variances; optional per-endpoint `n` column
# and per-endpoint `ev` (a list of event tables keyed by endpoint)
list(n = 60L, ev = ev,
     data = data.frame(DVID = c("cp", "cp", "cCSF"), TIME = c(1, 2, 2),
                       E = c(9.1, 7.4, 2.2), V = c(1.2, 0.9, 0.1)))

# joint (same subjects): ONE stacked covariance whose rows/cols align with
# the rows of `data` -- no `cross` blocks to assemble by hand
list(n = 60L, ev = ev, data = data.frame(DVID = ..., TIME = ..., E = ...),
     V = V_joint)

A study-level V (or an explicit joint = TRUE) marks the endpoints as same-subject; without one, each endpoint is an independent likelihood block. Endpoints are stacked in the order they first appear in data.

n_sim

Number of Monte Carlo samples per NLL evaluation.

outer_iter

Maximum inner optimiser iterations per phase.

sampling

Sampling method for eta draws: "sobol" (Sobol, default), "halton" (Halton), "torus" (Kronecker/torus), "lhs" (Latin hypercube), or "rnorm" (iid normal).

algorithm

nloptr algorithm string, or NULL (default) to pick the default that matches grad: "NLOPT_LD_LBFGS" with a gradient, "NLOPT_LN_BOBYQA" when grad = "none". Any algorithm reported by nloptr::nloptr.print.options() is accepted (e.g. "NLOPT_LD_MMA", "NLOPT_LN_NELDERMEAD"). An explicit algorithm is reconciled with grad: when grad = "none" a gradient-based algorithm (NLOPT_LD_* / NLOPT_GD_*) falls back to "NLOPT_LN_BOBYQA"; when a gradient is requested a derivative-free algorithm (NLOPT_LN_* / NLOPT_GN_*) turns the gradient off. Both emit a message.

maxeval

Maximum number of optimizer function evaluations.

ftol_rel

Relative function-value tolerance for convergence.

print

Print progress every this many evaluations (0 = silent).

omega_expansion

Inflate proposal Omega by this factor (>= 1).

seed

Random seed for reproducibility.

cores

Number of OpenMP threads for rxSolve(). Defaults to rxode2::rxCores(). rxSolve() parallelises over subjects, so this is the main speed lever for the MC estimators; when workers > 1 it is a total budget, split across the workers.

nDisplayProgress

Passed to rxSolve(): the solver shows its text progress bar only once a single solve exceeds this many subjects. The default (.Machine$integer.max) keeps the bar off, which is what you want for scripts, vignettes and logs; lower it (e.g. 1000L) to see solver progress during long interactive fits.

grad

Gradient mode for the inner optimiser: "analytical" (default, closed-form weight-path gradient), "none" (derivative-free BOBYQA), or "fd" (central finite differences). Note: "sens" is not available for the IRMC estimator.

kappa_method

Kappa correction method for models with non-mu-referenced struct thetas: "exact" (default, re-evaluates population prediction f(theta, 0) via rxSolve at each inner step), "linearized" (precomputes J = df/d(theta) once per outer iteration using f(theta, 0) as baseline — zero rxSolve per inner step), or "linearized_gh" (same linear approximation but baseline and Jacobian use Gauss-Hermite quadrature E_GH[f(theta, eta)] instead of f(theta, 0) — more accurate baseline at any IIV magnitude, still zero rxSolve per inner step).

kappa_n_nodes

Number of GH nodes per eta dimension for kappa_method = "linearized_gh" (default 5). Total quadrature points = kappa_n_nodes^n_eta. Ignored for other kappa methods.

grad_h

Step size for the inner optimiser's finite-difference gradient (grad = "fd"). Defaults to 1e-6, not the 1e-4 the other three controls use: the IRMC inner NLL is deterministic given fixed proposals, so there is no Monte Carlo noise to step over and the truncation-versus-noise balance that sets 1e-4 elsewhere does not apply. The inner step was a hard-coded 1e-6 until it was made to honour grad_h; inheriting the coarser default would have changed the gradient the loop was tuned for.

cov_h

Inner FD step for the gradient-based Hessian (only used when covMethod = "r" and grad != "none"). Each gradient evaluation has MC noise of order sigma / cov_h; the Hessian divides that noise by the outer step, giving total noise sigma / (cov_h * cov_h_outer * |p|). cov_h = 1e-3 balances truncation error and noise amplification. Increase to 1e-2 if the Hessian is non-positive definite.

cov_h_outer

Outer step scale for the numerical Hessian. The actual step for parameter p is max(|p|, 0.1) * cov_h_outer. Applied to both the gradient-FD Hessian (grad != "none") and the NLL-FD Hessian (grad = "none"). Default eps^(1/5) (~2.5e-3) is larger than the textbook eps^(1/4) to account for MC noise in NLL and gradient evaluations; empirically it matches the analytical (sensitivity-equation) Hessian ground truth. Increase (e.g. to 5e-3 or 1e-2) if the Hessian is non-positive definite.

phases

Numeric vector of box-constraint half-widths, one per phase. Phases progressively tighten the search region.

convcrit

Convergence criterion: phase ends when |approx - exact| < convcrit.

max_worse

Stop a phase after this many consecutive worsening iterations.

covMethod

"r,s" (the DEFAULT) computes the sandwich H^-1 J H^-1; "r" the numerical Hessian alone, 2H^-1; "none" skips the covariance. A study generated from a published model defaults to "none" and refuses an explicit covariance method because it has no sampling law. All three span the structural, residual-error and omega parameters, and are reported on the scale the estimates are printed on. admControl() documents what the sandwich is, why it is the conservative default, and why it is more sensitive than "r" to an ill-conditioned Hessian; the same implementation runs here. What does NOT carry over is the family coverage, because adirmc itself is narrower: the estimator accepts only add, prop, pow, combined1, combined2 and lnorm residuals, and "r,s" applies to all six. The count, beta, transform-both-sides, ordinal and ar() endpoints admControl() lists are refused by est = "adirmc" itself, before any covariance is reached – they are not models whose sandwich degrades to "r" here, they are models this estimator does not fit.

cov_n_sim

Number of MC samples for the covariance (Hessian) step. More samples reduce MC noise in NLL evaluations. The NLL-based Hessian (grad = "none") uses a central second difference of the NLL with the same Sobol sequence (CRN) at every perturbed point, so noise largely cancels and cov_n_sim = 10000 (default) is sufficient for most models.

n_restarts

Number of optimization restarts. Runs in parallel when workers > 1.

restart_sd

Standard deviation of structural theta perturbations for restart initialisation.

workers

Number of parallel workers for multi-restart. 1 (default) runs restarts sequentially. Values > 1 run the restarts on a pool of background R processes (mirai daemons), which behaves the same way on every platform. Requires the mirai package. Workers are stopped automatically after the restart phase so all cores are available for the Hessian step; if a fit is interrupted, admStopWorkers() cleans up any survivors.

rxControl

rxode2::rxControl() object. Created automatically when NULL.

calcTables, compress, ci, sigdigTable, optExpression, sumProd, literalFix

Passed to nlmixr2est::foceiControl() for the table/output machinery.

sigdig

Significant digits asked of the ODE solver, or NULL (the default) to leave rxode2's own solver tolerances alone. When set, it is passed to rxode2::rxSolve()'s own sigdig argument for every solve the estimator issues – rxode2 owns the mapping to atol/rtol and has changed it between releases, which is why the digits, not the tolerances, are what travels – and to nlmixr2est::foceiControl() for the post-fit tables.

It is a speed lever, and an opt-in one because it is not free. The estimators finite-difference the solve with steps of the same order: grad_h (1e-4), cov_h (1e-3) and cov_h_outer (~2.5e-3), while sigdig = 4 maps to a relative tolerance of ~1e-4 on current rxode2. Differencing a solution whose own noise is 1e-4 with a 1e-4 step returns noise, and it surfaces as a moved objective and an indefinite covariance Hessian (every SE reported NA) rather than as an error. Most worthwhile where the gradient is fully analytic and nothing differences the solve – adfoControl(grad = "analytical") measured ~4.8x faster at sigdig = 4 with standard errors unchanged to 4 significant figures. Elsewhere, compare the objective and the standard errors against NULL before relying on it. Table formatting is unaffected either way: sigdigTable defaults to 4 regardless.

addProp

How combined additive+proportional error is parameterised in the nlmixr2 output tables: "combined2" (default, variance form) or "combined1" (SD form). Has no effect on admixr2's own estimation; passed to nlmixr2est::foceiControl() for the table/output machinery only.

returnAdmr

If TRUE, return a plain list instead of a full nlmixr2 fit object (useful for debugging).

resid_nodes

Gauss-Hermite nodes used to integrate the RESIDUAL for a transform-both-sides endpoint (boxCox, yeoJohnson, logitNorm, probitNorm), where y = g(h(f) + sigma*eps) has no closed-form mean and variance. Ignored by every other error model, which has closed forms. Default 81. Measured worst-case relative error against an independent quadrature, over all four transforms and residual SD of 0.5, 1, 2 and 3: n = 15 gives 5.7e-2, 31 gives 4.5e-3, 81 gives 5.0e-5. The error is dominated by large residual SD; at SD <= 1, n = 31 already gives 1e-7 or better.

This is an ACCURACY dial, not a speed one. The quadrature is linear in resid_nodes in isolation (~50 us at 15, 300 us at 81 for an 8-row study) but negligible beside the ODE solve: a full NLL evaluation measured 0.750 s per 60 evaluations at BOTH 31 and 81 nodes. Raise it if you have a saturating endpoint with a large residual SD; there is little to gain by lowering it.

xtol_rel

Relative parameter tolerance for convergence (default sqrt(.Machine$double.eps)).

...

Additional arguments (none allowed; triggers an error).

Value

An object of class adirmcControl.

Details

Multi-compartment fits (a study observations list with several observed outputs) are not supported by adirmc; use est = "admc", "adfo", or "adgh" for those. Single-output studies are fit as usual.

Examples

# Inspect defaults
ctl <- adirmcControl()
ctl$phases
#> [1] 2.00 1.00 0.50 0.01
ctl$omega_expansion
#> [1] 1

# Tighter phases, more restarts
ctl2 <- adirmcControl(
  n_sim           = 1000L,
  omega_expansion = 1.5,
  phases          = c(2, 1, 0.5, 0.01),
  n_restarts      = 3L
)

# \donttest{
library(rxode2)
library(nlmixr2)

data("examplomycin")
obs   <- examplomycin[examplomycin$EVID == 0, ]
obs   <- obs[order(obs$ID, obs$TIME), ]
times <- sort(unique(obs$TIME))
ids   <- unique(obs$ID)
dv_mat <- do.call(rbind, lapply(ids, function(i) {
  sub <- obs[obs$ID == i, ]; sub$DV[order(sub$TIME)]
}))
E <- colMeans(dv_mat)
V <- diag(diag(cov.wt(dv_mat, method = "ML")$cov))

pk_model <- function() {
  ini({
    tcl <- log(5);  tv1 <- log(12); tv2 <- log(25)
    tq  <- log(12); tka <- log(1.2)
    prop.sd <- c(0, 0.2)
    eta.cl ~ 0.09; eta.v1 ~ 0.09; eta.v2 ~ 0.09
    eta.q  ~ 0.09; eta.ka ~ 0.09
  })
  model({
    cl <- exp(tcl + eta.cl); v1 <- exp(tv1 + eta.v1)
    v2 <- exp(tv2 + eta.v2); q  <- exp(tq  + eta.q)
    ka <- exp(tka + eta.ka)
    d/dt(depot)      <- -ka * depot
    d/dt(central)    <- ka * depot - (cl/v1 + q/v1) * central + (q/v2) * peripheral
    d/dt(peripheral) <- (q/v1) * central - (q/v2) * peripheral
    cp <- central / v1
    cp ~ prop(prop.sd)
  })
}

fit <- nlmixr2(
  pk_model, admData(), est = "adirmc",
  control = adirmcControl(
    studies = list(study1 = list(E = E, V = V, n = length(ids),
                                 times = times, ev = et(amt = 100))),
    n_sim   = 500L
  )
)
#>  
#>  
#>  
#>  
#> ℹ parameter labels from comments are typically ignored in non-interactive mode
#> ℹ Need to run with the source intact to parse comments
#> → loading into symengine environment...
#> → pruning branches (`if`/`else`) of full model...
#> ✔ done
#> → calculate sensitivities
#> → finding duplicate expressions in admixr2 sensitivity model...
#>  
#>  
#>  
#>  
#>  
#>  
#> === admixr2: Aggregate Data Modeling (IR-MC) ===
#>   Studies: 1 | MC samples: 500 | Phases: 4 | Iters/phase: 50 | Expansion: 1.00 | Grad: analytic+Sens-Hessian | Restarts: 1
#> +----------+----------+----------+----------+----------+----------+----------+----------+----------+----------+----------+----------+----------+
#> |          |     -2LL |      tcl |      tv1 |      tv2 |       tq |      tka |  prop.sd |   eta.cl |   eta.v1 |   eta.v2 |    eta.q |   eta.ka |
#> +-- Phase 1: Wide (+/-2.00) -------------------------------------------------------------------------------------------------------------------+
#> | 0001     |  2275.01 |    4.164 |    2.486 |    31.23 |    8.951 |   0.7664 |   0.1942 |   0.2236 |    0.665 |   0.2905 |  0.02583 |   0.1075 |
#> | 0002     | -1238.39 |    4.907 |    8.253 |    31.55 |    9.105 |   0.8141 |    0.191 |   0.1114 |     0.09 |  0.07618 |  0.05042 |   0.1619 |
#> | 0003     | -1265.61 |    4.918 |    7.943 |    31.88 |    8.851 |   0.7975 |   0.1735 |    0.126 |  0.06732 |   0.1044 |  0.02434 |   0.1414 |
#> | 0004     | -1263.03 |     4.96 |    7.856 |    31.51 |    8.803 |    0.798 |   0.1857 |   0.1133 |  0.08097 |  0.08228 | 0.006206 |   0.1283 |
#> | 0005     | -1265.63 |     4.92 |    7.866 |    31.61 |    8.843 |   0.7929 |   0.1837 |   0.1236 |  0.07812 |    0.098 | 0.006491 |     0.13 |
#> | 0006     | -1266.36 |    4.938 |    7.897 |    31.54 |    8.866 |   0.7967 |   0.1861 |   0.1174 |  0.07976 |  0.09014 | 0.007374 |   0.1267 |
#> | 0007     | -1266.39 |    4.929 |    7.883 |    31.57 |    8.851 |   0.7949 |   0.1848 |   0.1203 |  0.07921 |  0.09387 | 0.007537 |   0.1296 |
#> | 0008     | -1266.46 |    4.937 |    7.872 |    31.58 |    8.844 |    0.794 |   0.1851 |   0.1188 |  0.07817 |  0.09111 | 0.007357 |   0.1291 |
#> | 0009     | -1266.48 |    4.933 |    7.871 |    31.59 |    8.844 |   0.7939 |   0.1849 |   0.1194 |  0.07861 |   0.0925 | 0.007237 |   0.1295 |
#> | 0010     | -1266.48 |    4.934 |    7.874 |    31.58 |    8.847 |   0.7943 |   0.1851 |   0.1191 |  0.07856 |  0.09187 | 0.007266 |    0.129 |
#> | 0011     | -1266.48 |    4.933 |    7.875 |    31.58 |    8.847 |   0.7943 |    0.185 |   0.1193 |   0.0787 |  0.09228 | 0.007291 |   0.1292 |
#> | 0012     | -1266.48 |    4.934 |    7.875 |    31.58 |    8.847 |   0.7943 |   0.1851 |   0.1192 |  0.07861 |  0.09204 | 0.007304 |   0.1291 |
#> | 0013     | -1266.48 |    4.934 |    7.874 |    31.58 |    8.846 |   0.7942 |    0.185 |   0.1193 |  0.07863 |  0.09215 | 0.007296 |   0.1292 |
#> | 0014     | -1266.48 |    4.934 |    7.874 |    31.58 |    8.846 |   0.7942 |   0.1851 |   0.1192 |  0.07861 |  0.09209 | 0.007297 |   0.1291 |
#> | 0015     | -1266.48 |    4.934 |    7.874 |    31.58 |    8.846 |   0.7942 |    0.185 |   0.1192 |  0.07862 |  0.09213 | 0.007296 |   0.1292 |
#> | 0016     | -1266.48 |    4.934 |    7.874 |    31.58 |    8.846 |   0.7942 |    0.185 |   0.1192 |  0.07861 |  0.09211 | 0.007296 |   0.1291 |
#> | 0017 ✓   | -1266.48 |    4.934 |    7.874 |    31.58 |    8.846 |   0.7942 |    0.185 |   0.1192 |  0.07862 |  0.09211 | 0.007293 |   0.1292 |
#> +-- Phase 2: Focused (+/-1.00) ----------------------------------------------------------------------------------------------------------------+
#> | 0018     | -1266.48 |    4.934 |    7.874 |    31.58 |    8.846 |   0.7942 |    0.185 |   0.1192 |  0.07861 |  0.09211 | 0.007296 |   0.1291 |
#> | 0019 ✓   | -1266.48 |    4.934 |    7.874 |    31.58 |    8.846 |   0.7942 |    0.185 |   0.1192 |  0.07862 |  0.09211 | 0.007293 |   0.1292 |
#> +-- Phase 3: Fine-tuning (+/-0.50) ------------------------------------------------------------------------------------------------------------+
#> | 0020     | -1266.48 |    4.934 |    7.874 |    31.58 |    8.846 |   0.7942 |    0.185 |   0.1192 |  0.07861 |  0.09211 | 0.007296 |   0.1291 |
#> | 0021 ✓   | -1266.48 |    4.934 |    7.874 |    31.58 |    8.846 |   0.7942 |    0.185 |   0.1192 |  0.07862 |  0.09211 | 0.007293 |   0.1292 |
#> +-- Phase 4: Precision (+/-0.01) --------------------------------------------------------------------------------------------------------------+
#> | 0022     | -1266.48 |    4.934 |    7.874 |    31.58 |    8.846 |   0.7942 |   0.1851 |   0.1192 |  0.07862 |  0.09211 | 0.007296 |   0.1291 |
#> | 0023     | -1266.48 |    4.934 |    7.874 |    31.58 |    8.846 |   0.7942 |    0.185 |   0.1192 |  0.07862 |  0.09212 | 0.007294 |   0.1292 |
#> | 0024 ✓   | -1266.48 |    4.934 |    7.874 |    31.58 |    8.846 |   0.7942 |    0.185 |   0.1192 |  0.07862 |  0.09211 | 0.007294 |   0.1291 |
#> | 2.8 sec  |          |          |          |          |          |          |          |          |          |          |          |          |
#>   Computing covariance (R method, MC NLL, Sens-Hessian, sandwich, 12 gradient evaluations)
#> → compress origData in nlmixr2 object, save 1160
#>  
#>  
print(fit)
#> ── nlmixr² adirmc ──
#> 
#>             OBJF       AIC       BIC Log-likelihood
#> adirmc -1266.484 -1244.484 -1173.953       633.2418
#> 
#> ── Time (sec fit$time): ──
#> 
#>   optimize covariance other elapsed
#> 1    2.786     17.654     0   20.44
#> 
#> ── Population Parameters (fit$parFixed or fit$parFixedDf): ──
#> 
#>            Est.      SE  %RSE Back-transformed(95%CI) BSV(CV%) Shrink(SD)%
#> tcl       1.596 0.05289 3.314    4.934 (4.448, 5.473)    35.59         NaN
#> tv1       2.064  0.4762 23.07    7.874 (3.097, 20.02)    28.60         NaN
#> tv2       3.453  0.1869 5.412    31.58 (21.90, 45.55)    31.07         NaN
#> tq        2.180  0.1536 7.045    8.846 (6.547, 11.95)    8.557         NaN
#> tka     -0.2304  0.4307 186.9  0.7942 (0.3415, 1.847)    37.13         NaN
#> prop.sd  0.1850 0.01205 6.514 0.1850 (0.1614, 0.2087)                     
#>  
#>   Covariance Type (fit$covMethod): r,s
#>   Some strong fixed parameter correlations exist (fit$cor) :
#>                 cor:tv1,tcl             cor:tv2,tcl              cor:tq,tcl 
#>                  0.932                  -0.946                   0.933  
#>             cor:tka,tcl         cor:prop.sd,tcl       cor:om.eta.cl,tcl 
#>                  0.933                   0.737                  -0.750  
#>       cor:om.eta.v1,tcl       cor:om.eta.v2,tcl        cor:om.eta.q,tcl 
#>                 -0.563                   0.115                    0.919  
#>       cor:om.eta.ka,tcl             cor:tv2,tv1              cor:tq,tv1 
#>                  0.808                  -0.989                   0.992  
#>             cor:tka,tv1         cor:prop.sd,tv1       cor:om.eta.cl,tv1 
#>                  0.999                   0.782                  -0.740  
#>       cor:om.eta.v1,tv1       cor:om.eta.v2,tv1        cor:om.eta.q,tv1 
#>                 -0.623                   0.118                    0.982  
#>       cor:om.eta.ka,tv1              cor:tq,tv2             cor:tka,tv2 
#>                  0.871                  -0.985                  -0.989  
#>         cor:prop.sd,tv2       cor:om.eta.cl,tv2       cor:om.eta.v1,tv2 
#>                 -0.775                   0.764                   0.602  
#>       cor:om.eta.v2,tv2        cor:om.eta.q,tv2       cor:om.eta.ka,tv2 
#>                 -0.140                   -0.974                  -0.865  
#>              cor:tka,tq          cor:prop.sd,tq        cor:om.eta.cl,tq 
#>                  0.993                   0.782                  -0.741  
#>        cor:om.eta.v1,tq        cor:om.eta.v2,tq         cor:om.eta.q,tq 
#>                 -0.619                   0.173                    0.975  
#>        cor:om.eta.ka,tq         cor:prop.sd,tka       cor:om.eta.cl,tka 
#>                  0.851                   0.784                  -0.741  
#>       cor:om.eta.v1,tka       cor:om.eta.v2,tka        cor:om.eta.q,tka 
#>                 -0.622                   0.121                    0.981  
#>       cor:om.eta.ka,tka   cor:om.eta.cl,prop.sd   cor:om.eta.v1,prop.sd 
#>                  0.870                  -0.721                  -0.625  
#>   cor:om.eta.v2,prop.sd    cor:om.eta.q,prop.sd   cor:om.eta.ka,prop.sd 
#>                 0.0691                    0.841                   0.656  
#> cor:om.eta.v1,om.eta.cl cor:om.eta.v2,om.eta.cl  cor:om.eta.q,om.eta.cl 
#>                  0.499                  -0.172                   -0.747  
#> cor:om.eta.ka,om.eta.cl cor:om.eta.v2,om.eta.v1  cor:om.eta.q,om.eta.v1 
#>                 -0.657                  0.0185                   -0.669  
#> cor:om.eta.ka,om.eta.v1  cor:om.eta.q,om.eta.v2 cor:om.eta.ka,om.eta.v2 
#>                 -0.424                  0.0617                   0.0332   
#>  cor:om.eta.ka,om.eta.q 
#>                  0.843  
#>  
#> 
#>   No correlations in between subject variability (BSV) matrix
#>   Full BSV covariance (fit$omega) or correlation (fit$omegaR; diagonals=SDs) 
#>   Distribution stats (mean/skewness/kurtosis/p-value) available in fit$shrink 
#>   Information about run found (fit$runInfo):
#>    • covMethod = "r,s": the Hessian is ill-conditioned (rcond 1.0e-05, cond 9.57e+04), and the sandwich inverts it twice where "r" inverts it once -- so the correction is amplified quadratically in the weakly-identified direction, which loads mainly on `logchol_eta.q`. Check that parameter's relative standard error before reading its "r,s" value as a finding; the well-determined parameters are unaffected. 
#>   Censoring (fit$censInformation): No censoring
#>   Minimization message (fit$message):  
#>     NLOPT_XTOL_REACHED: Optimization stopped because xtol_rel or xtol_abs (above) was reached. 
# }