Control for lbfgsb3c estimation method in nlmixr2

Usage

lbfgsb3cControl(
  trace = 0,
  factr = 1e+07,
  pgtol = 0,
  abstol = 0,
  reltol = 0,
  lmm = 5L,
  maxit = 10000L,
  returnLbfgsb3c = FALSE,
  stickyRecalcN = 4,
  maxOdeRecalc = 5,
  odeRecalcFactor = 10^(0.5),
  indTolRelax = TRUE,
  useColor = NULL,
  printNcol = NULL,
  print = 1L,
  normType = c("rescale2", "mean", "rescale", "std", "len", "constant"),
  scaleType = c("nlmixr2", "norm", "mult", "multAdd"),
  scaleCmax = 1e+05,
  scaleCmin = 1e-05,
  scaleC = NULL,
  scaleTo = 1,
  gradTo = 1,
  rxControl = NULL,
  optExpression = TRUE,
  sumProd = FALSE,
  literalFix = TRUE,
  literalFixRes = TRUE,
  addProp = c("combined2", "combined1"),
  eventSens = c("jump", "fd"),
  sensMethod = c("default", "forward", "adjoint"),
  calcTables = TRUE,
  compress = FALSE,
  covMethod = c("r", ""),
  adjObf = TRUE,
  ci = 0.95,
  sigdig = 4,
  sigdigTable = NULL,
  ...
)

Arguments

trace: If positive, print tracing information; higher values give more detail (see source for "L-BFGS-B" trace levels).
factr: Convergence tolerance factor for "L-BFGS-B"; converges when the objective reduction is within this factor of machine tolerance (default 1e7, i.e. ~1e-8).
pgtol: Tolerance on the projected gradient for "L-BFGS-B"; 0 (default) suppresses the check.
abstol: Absolute x-value tolerance for "L-BFGS-B"; 0 (default) suppresses the check.
reltol: Relative x-value tolerance for "L-BFGS-B"; 0 (default) suppresses the check.
lmm: Number of BFGS updates retained in "L-BFGS-B" (default 5).
maxit: maximum number of iterations.
returnLbfgsb3c: return the lbfgsb3c output instead of the nlmixr2 fit
stickyRecalcN: The number of bad ODE solves before reducing the atol/rtol for the rest of the problem.
maxOdeRecalc: Maximum number of times to reduce the ODE tolerances and try to resolve the system if there was a bad ODE solve.
odeRecalcFactor: The ODE recalculation factor when ODE solving goes bad, this is the factor the rtol/atol is reduced
indTolRelax: When `TRUE` (default), only subjects whose ODE solve produced NaN/Inf have their tolerances relaxed, and the relaxed tolerance persists across optimizer calls (sticky). When `FALSE`, all subjects have their tolerances relaxed on each retry and tolerances are reset afterward.
useColor: Logical (or `NULL`) emit ANSI bold/color escapes in the iteration print. `NULL` (default) defers to [crayon::has_color()].
printNcol: Integer (or `NULL`) parameter columns per row before wrapping. `NULL` (default) uses `floor((getOption("width") - 23) / 12)`.
print: Either a scalar print-frequency (`0` = suppress, `1` (default) = every evaluation, `N` = every Nth), OR a pre-built [iterPrintControl()] object. Equivalent to `iterPrintControl(every = print, ncol = printNcol, useColor = useColor)`.
normType: Parameter normalization/scaling used to get scaled initial values for scaleType, of the form Vscaled = (Vunscaled-C1)/C2 (see Feature Scaling; rescale2 follows the OptdesX manual): "rescale2" scales all parameters to (-1, 1); "rescale" (min-max) scales to (0, 1); "mean" centers on the mean with range (0, 1); "std" standardizes by mean/sd; "len" scales to unit (Euclidean) length; "constant" performs no normalization (C1=0, C2=1).
scaleType: The scaling scheme for nlmixr2: "nlmixr2" (default) scales as (current-init)*scaleC[i] + scaleTo, with scaleTo from normType and scales from scaleC; "norm" uses the simple scaling from normType; "mult" scales multiplicatively as current/init*scaleTo; "multAdd" scales linearly ((current-init)+scaleTo) for parameters in an exponential block (e.g. exp(theta)) and multiplicatively otherwise.
scaleCmax: Maximum value of the scaleC to prevent overflow.
scaleCmin: Minimum value of the scaleC to prevent underflow.
scaleC: Scaling constant used with scaleType="nlmixr2"; when not specified, chosen by parameter type to keep gradient sizes similar on a log scale: `1` for exp()-transformed/power/boxCox/ yeoJohnson parameters, `0.5*abs(est)` for additive/proportional/ lognormal error parameters, `abs(1/digamma(est+1))` for factorials, and `log(abs(est))*abs(est)` for log-scale parameters. May be set explicitly per parameter if these defaults don't apply well.
scaleTo: Scale the initial parameter estimate to this value. By default this is 1. When zero or below, no scaling is performed.
gradTo: this is the factor that the gradient is scaled to before optimizing. This only works with scaleType="nlmixr2".
rxControl: `rxode2` ODE solving options during fitting, created with `rxControl()`
optExpression: Optimize the rxode2 expression to speed up calculation. By default this is turned on.
sumProd: Is a boolean indicating if the model should change multiplication to high precision multiplication and sums to high precision sums using the PreciseSums package. By default this is FALSE.
literalFix: boolean, substitute fixed population values as literals and re-adjust ui and parameter estimates after optimization; Default is `TRUE`.
literalFixRes: boolean, substitute fixed population values as literals and re-adjust ui and parameter estimates after optimization; Default is `TRUE`.
addProp: Type of additive-plus-proportional error: `"combined1"`, where standard deviations add: $$y = f + (a + b\times f^c) \times \varepsilon$$; or `"combined2"`, where variances add: $$y = f + \sqrt{a^2 + b^2\times f^{2\times c}} \times \varepsilon$$. Here y = observed, f = predicted, a = additive sd, b = proportional/power sd, c = power exponent (1 in the proportional case).
eventSens: Controls how dosing/event-parameter (`alag`, `F`, `rate`, `dur`) sensitivities are computed for THETA/ETA gradients: `"jump"` (default) uses rxode2's analytic event sensitivities; `"fd"` uses the legacy finite-difference behavior.
sensMethod: Method used to compute the ODE parameter sensitivities: `"default"` (the default) defers to the global option `getOption("nlmixr2est.adjoint")` (itself `"forward"` by default); `"forward"` uses the classic variational (forward) sensitivity ODEs; `"adjoint"` uses the in-engine discrete adjoint with the matching adjoint (`s`) method.
calcTables: This boolean is to determine if the foceiFit will calculate tables. By default this is TRUE
compress: Should the object have compressed items
covMethod: Method for calculating the covariance. "analytic" (the default) uses the exact analytic observed-information R-matrix (reported as $R^{-1}$) and additionally returns the residual and Omega standard errors; it covers FOCEI/FOCE fits with additive, proportional, or combined error, mu-referenced/covariate/other structural parameters (and non-mu-referenced etas), and SD-scale inter-occasion variability, and emits a message and falls back to the finite-difference Hessian for anything out of scope (FO, nAGQ > 1, censoring, DV-transformed error, bounded-parameter transforms, a structural theta shared by two etas, non-SD iovXform, or a pure-proportional variance that vanishes at a near-zero prediction). The finite-difference methods use R (the Hessian) and S (the sum of individual gradient cross-products at the empirical Bayes estimates): "r,s" sandwich (solve(R)%*%S%*%solve(R)), "r" Hessian-based (solve(R)), "s" cross-product-based (solve(S)), or "" to skip the covariance step.
adjObf: is a boolean to indicate if the objective function should be adjusted to be closer to NONMEM's default objective function. By default this is TRUE
ci: Confidence level for some tables. By default this is 0.95 or 95% confidence.
sigdig: Optimization significant digits; controls the inner/outer optimization tolerance (10^-sigdig), ODE solver tolerance (0.5*10^(-sigdig-2), or 0.5*10^(-sigdig-1.5) for sensitivity/steady-state with liblsoda), and boundary check tolerance (5*10^(-sigdig+1)).
sigdigTable: Significant digits in the final output table. If not specified, then it matches the significant digits in the `sigdig` optimization algorithm. If `sigdig` is NULL, use 3.
...: Ignored parameters

Value

bobqya control structure

Author

Matthew L. Fidler

Examples


# \donttest{
# A logit regression example with emax model

dsn <- data.frame(i=1:1000)
dsn$time <- exp(rnorm(1000))
dsn$DV=rbinom(1000,1,exp(-1+dsn$time)/(1+exp(-1+dsn$time)))

mod <- function() {
 ini({
   E0 <- 0.5
   Em <- 0.5
   E50 <- 2
   g <- fix(2)
 })
 model({
   v <- E0+Em*time^g/(E50^g+time^g)
   ll(bin) ~ DV * v - log(1 + exp(v))
 })
}

fit2 <- nlmixr(mod, dsn, est="lbfgsb3c")
#>  
#>  
#>  
#>  
#> 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 population log-likelihood model...
#> ✔ done
#> → calculate ∂(f)/∂(θ)
#> → finding duplicate expressions in nlm llik gradient...
#> → optimizing duplicate expressions in nlm llik gradient...
#> → finding duplicate expressions in nlm pred-only...
#> → optimizing duplicate expressions in nlm pred-only...
#>  
#>  
#>  
#>  
#> → calculating covariance
#> ✔ done
#> → loading into symengine environment...
#> → pruning branches (`if`/`else`) of full model...
#> ✔ done
#> → finding duplicate expressions in EBE model...
#> → optimizing duplicate expressions in EBE model...
#> → compiling EBE model...
#>  
#>  
#> ✔ done
#> → Calculating residuals/tables
#> ✔ done

print(fit2)
#> ── nlmixr² log-likelihood lbfgsb3c ──
#> 
#>           OBJF      AIC      BIC Log-likelihood Condition#(Cov) Condition#(Cor)
#> lPop -704.4138 1139.463 1154.187      -566.7316        3319.747        164.2639
#> 
#> ── Time (sec $time): ──
#> 
#>             setup optimize covariance preprocess postprocess table compress
#> elapsed 0.3344189 1.327431  7.952e-06      0.039       0.012 0.025    0.001
#>              other
#> elapsed 0.08514241
#> 
#> ── ($parFixed or $parFixedDf): ──
#> 
#>       Est.     SE  %RSE  Back-transformed(95%CI) BSV(SD) Shrink(SD)%
#> E0  -0.627 0.2189 34.92 -0.627 (-1.056, -0.1979)                    
#> Em    9.06  7.546 83.29     9.06 (-5.729, 23.85)                    
#> E50  4.027  2.418 60.04   4.027 (-0.7119, 8.766)                    
#> g        2  FIXED FIXED                        2                    
#>  
#>   Covariance Type ($covMethod): r
#>   Censoring ($censInformation): No censoring
#>   Minimization message ($message):  
#>     CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH 
#> 
#> ── Fit Data (object is a modified tibble): ──
#> # A tibble: 1,000 × 5
#>   ID      TIME    DV  IPRED      v
#>   <fct>  <dbl> <dbl>  <dbl>  <dbl>
#> 1 1     0.0249     0 -0.428 -0.627
#> 2 1     0.0403     0 -0.428 -0.626
#> 3 1     0.0864     1 -1.05  -0.623
#> # ℹ 997 more rows

# you can also get the nlm output with fit2$lbfgsb3c

fit2$lbfgsb3c
#> $par
#>         E0         Em        E50 
#> -0.6270132  9.0601102  4.0272143 
#> 
#> $grad
#> [1]  2.919944e-08 -1.162765e-07  1.310491e-07
#> 
#> $value
#> [1] 566.7316
#> 
#> $counts
#> [1] 19 19
#> 
#> $convergence
#> [1] 0
#> 
#> $message
#> [1] "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH"
#> 
#> $scaleC
#> [1] 0.00289795 0.04009648 0.03492545
#> 
#> $par.scaled
#>         E0         Em        E50 
#> -389.90016  212.48784   59.04404 
#> 
#> $hessian
#>               E0           Em          E50
#> E0   0.001657843  0.001575858 -0.005137211
#> Em   0.001575858  0.003667536 -0.010625243
#> E50 -0.005137211 -0.010625243  0.031950367
#> 
#> $cov.scaled
#>           E0        Em       E50
#> E0  5707.768  5643.188  2794.404
#> Em  5643.188 35416.278 12685.200
#> E50 2794.404 12685.200  4793.021
#> 
#> $r
#>                E0            Em          E50
#> E0   0.0008289213  0.0007879291 -0.002568605
#> Em   0.0007879291  0.0018337679 -0.005312621
#> E50 -0.0025686054 -0.0053126214  0.015975184
#> 

# The nlm control has been modified slightly to include
# extra components and name the parameters
# }