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Control Values for nlme Fit with extra options for nlmixr

Source: R/nlme.R
nlmixr2NlmeControl.Rd

The values supplied in the function call replace the defaults and a list with all possible arguments is returned. The returned list is used as the ‘control’ argument to the ‘nlme’ function.

Usage

nlmixr2NlmeControl(
  maxIter = 100,
  pnlsMaxIter = 100,
  msMaxIter = 100,
  minScale = 0.001,
  tolerance = 1e-05,
  niterEM = 25,
  pnlsTol = 0.001,
  msTol = 1e-06,
  returnObject = FALSE,
  msVerbose = FALSE,
  msWarnNoConv = TRUE,
  gradHess = TRUE,
  apVar = TRUE,
  .relStep = .Machine$double.eps^(1/3),
  minAbsParApVar = 0.05,
  opt = c("nlminb", "nlm"),
  natural = TRUE,
  sigma = NULL,
  optExpression = TRUE,
  literalFix = TRUE,
  sumProd = FALSE,
  rxControl = NULL,
  method = c("ML", "REML"),
  random = NULL,
  fixed = NULL,
  weights = NULL,
  verbose = TRUE,
  returnNlme = FALSE,
  addProp = c("combined2", "combined1"),
  calcTables = TRUE,
  compress = TRUE,
  adjObf = TRUE,
  ci = 0.95,
  sigdig = 4,
  sigdigTable = NULL,
  muRefCovAlg = TRUE,
  ...
)

nlmeControl(
  maxIter = 100,
  pnlsMaxIter = 100,
  msMaxIter = 100,
  minScale = 0.001,
  tolerance = 1e-05,
  niterEM = 25,
  pnlsTol = 0.001,
  msTol = 1e-06,
  returnObject = FALSE,
  msVerbose = FALSE,
  msWarnNoConv = TRUE,
  gradHess = TRUE,
  apVar = TRUE,
  .relStep = .Machine$double.eps^(1/3),
  minAbsParApVar = 0.05,
  opt = c("nlminb", "nlm"),
  natural = TRUE,
  sigma = NULL,
  optExpression = TRUE,
  literalFix = TRUE,
  sumProd = FALSE,
  rxControl = NULL,
  method = c("ML", "REML"),
  random = NULL,
  fixed = NULL,
  weights = NULL,
  verbose = TRUE,
  returnNlme = FALSE,
  addProp = c("combined2", "combined1"),
  calcTables = TRUE,
  compress = TRUE,
  adjObf = TRUE,
  ci = 0.95,
  sigdig = 4,
  sigdigTable = NULL,
  muRefCovAlg = TRUE,
  ...
)

Arguments

maxIter

maximum number of iterations for the nlme optimization algorithm. Default is 50.

pnlsMaxIter

maximum number of iterations for the PNLS optimization step inside the nlme optimization. Default is 7.

msMaxIter

maximum number of iterations for nlminb (iter.max) or the nlm (iterlim, from the 10-th step) optimization step inside the nlme optimization. Default is 50 (which may be too small for e.g. for overparametrized cases).

minScale

minimum factor by which to shrink the default step size in an attempt to decrease the sum of squares in the PNLS step. Default 0.001.

tolerance

tolerance for the convergence criterion in the nlme algorithm. Default is 1e-6.

niterEM

number of iterations for the EM algorithm used to refine the initial estimates of the random effects variance-covariance coefficients. Default is 25.

pnlsTol

tolerance for the convergence criterion in PNLS step. Default is 1e-3.

msTol

tolerance for the convergence criterion in nlm, passed as the gradtol argument to the function (see documentation on nlm). Default is 1e-7.

returnObject

a logical value indicating whether the fitted object should be returned when the maximum number of iterations is reached without convergence of the algorithm. Default is FALSE.

msVerbose

a logical value passed as the trace to nlminb(.., control= list(trace = *, ..)) or as argument print.level to nlm(). Default is FALSE.

msWarnNoConv

logical indicating if a warning should be signalled whenever the minimization (by opt) in the LME step does not converge; defaults to TRUE.

gradHess

a logical value indicating whether numerical gradient vectors and Hessian matrices of the log-likelihood function should be used in the nlm optimization. This option is only available when the correlation structure (corStruct) and the variance function structure (varFunc) have no "varying" parameters and the pdMat classes used in the random effects structure are pdSymm (general positive-definite), pdDiag (diagonal), pdIdent (multiple of the identity), or pdCompSymm (compound symmetry). Default is TRUE.

apVar

a logical value indicating whether the approximate covariance matrix of the variance-covariance parameters should be calculated. Default is TRUE.

.relStep

relative step for numerical derivatives calculations. Default is .Machine$double.eps^(1/3).

minAbsParApVar

numeric value - minimum absolute parameter value in the approximate variance calculation. The default is 0.05.

opt

the optimizer to be used, either "nlminb" (the default) or "nlm".

natural

a logical value indicating whether the pdNatural parametrization should be used for general positive-definite matrices (pdSymm) in reStruct, when the approximate covariance matrix of the estimators is calculated. Default is TRUE.

sigma

optionally a positive number to fix the residual error at. If NULL, as by default, or 0, sigma is estimated.

optExpression

Optimize the rxode2 expression to speed up calculation. By default this is turned on.

literalFix

boolean, substitute fixed population values as literals and re-adjust ui and parameter estimates after optimization; Default is `TRUE`.

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.

rxControl

`rxode2` ODE solving options during fitting, created with `rxControl()`

method

a character string. If "REML" the model is fit by maximizing the restricted log-likelihood. If "ML" the log-likelihood is maximized. Defaults to "ML".

random

optionally, any of the following: (i) a two-sided formula of the form r1+...+rn~x1+...+xm | g1/.../gQ, with r1,...,rn naming parameters included on the right hand side of model, x1+...+xm specifying the random-effects model for these parameters and g1/.../gQ the grouping structure (Q may be equal to 1, in which case no / is required). The random effects formula will be repeated for all levels of grouping, in the case of multiple levels of grouping; (ii) a two-sided formula of the form r1+...+rn~x1+..+xm, a list of two-sided formulas of the form r1~x1+...+xm, with possibly different random-effects models for different parameters, a pdMat object with a two-sided formula, or list of two-sided formulas (i.e. a non-NULL value for formula(random)), or a list of pdMat objects with two-sided formulas, or lists of two-sided formulas. In this case, the grouping structure formula will be given in groups, or derived from the data used to fit the nonlinear mixed-effects model, which should inherit from class groupedData,; (iii) a named list of formulas, lists of formulas, or pdMat objects as in (ii), with the grouping factors as names. The order of nesting will be assumed the same as the order of the order of the elements in the list; (iv) an reStruct object. See the documentation on pdClasses for a description of the available pdMat classes. Defaults to fixed, resulting in all fixed effects having also random effects.

fixed

a two-sided linear formula of the form f1+...+fn~x1+...+xm, or a list of two-sided formulas of the form f1~x1+...+xm, with possibly different models for different parameters. The f1,...,fn are the names of parameters included on the right hand side of model and the x1+...+xm expressions define linear models for these parameters (when the left hand side of the formula contains several parameters, they all are assumed to follow the same linear model, described by the right hand side expression). A 1 on the right hand side of the formula(s) indicates a single fixed effects for the corresponding parameter(s).

weights

an optional varFunc object or one-sided formula describing the within-group heteroscedasticity structure. If given as a formula, it is used as the argument to varFixed, corresponding to fixed variance weights. See the documentation on varClasses for a description of the available varFunc classes. Defaults to NULL, corresponding to homoscedastic within-group errors.

verbose

an optional logical value. If TRUE information on the evolution of the iterative algorithm is printed. Default is FALSE.

returnNlme

Returns the nlme object instead of the nlmixr object (by default FALSE). If any of the nlme specific options of `random`, `fixed`, `sens`, the nlme object is returned

addProp

specifies the type of additive plus proportional errors, the one where standard deviations add (combined1) or the type where the variances add (combined2).

The combined1 error type can be described by the following equation:

$$y = f + (a + b\times f^c) \times \varepsilon$$

The combined2 error model can be described by the following equation:

$$y = f + \sqrt{a^2 + b^2\times f^{2\times c}} \times \varepsilon$$

Where:

- y represents the observed value

- f represents the predicted value

- a is the additive standard deviation

- b is the proportional/power standard deviation

- c is the power exponent (in the proportional case c=1)

calcTables

This boolean is to determine if the foceiFit will calculate tables. By default this is TRUE

compress

Should the object have compressed items

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. This controls:

  • The tolerance of the inner and outer optimization is 10^-sigdig

  • The tolerance of the ODE solvers is 0.5*10^(-sigdig-2); For the sensitivity equations and steady-state solutions the default is 0.5*10^(-sigdig-1.5) (sensitivity changes only applicable for liblsoda)

  • The tolerance of the boundary check is 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.

muRefCovAlg

This controls if algebraic expressions that can be mu-referenced are treated as mu-referenced covariates by:

1. Creating a internal data-variable `nlmixrMuDerCov#` for each algebraic mu-referenced expression

2. Change the algebraic expression to `nlmixrMuDerCov# * mu_cov_theta`

3. Use the internal mu-referenced covariate for saem

4. After optimization is completed, replace `model()` with old `model()` expression

5. Remove `nlmixrMuDerCov#` from nlmix2 output

In general, these covariates should be more accurate since it changes the system to a linear compartment model. Therefore, by default this is `TRUE`.

...

Further, named control arguments to be passed to nlminb (apart from trace and iter.max mentioned above), where used (eval.max and those from abs.tol down).

Value

a nlmixr-nlme list

See also

Other Estimation control: foceiControl(), saemControl()

Examples

nlmeControl()
#> $maxIter
#> [1] 100
#> 
#> $pnlsMaxIter
#> [1] 100
#> 
#> $msMaxIter
#> [1] 100
#> 
#> $minScale
#> [1] 0.001
#> 
#> $tolerance
#> [1] 1e-05
#> 
#> $niterEM
#> [1] 25
#> 
#> $pnlsTol
#> [1] 0.001
#> 
#> $msTol
#> [1] 1e-06
#> 
#> $returnObject
#> [1] FALSE
#> 
#> $msVerbose
#> [1] FALSE
#> 
#> $msWarnNoConv
#> [1] TRUE
#> 
#> $gradHess
#> [1] TRUE
#> 
#> $apVar
#> [1] TRUE
#> 
#> $.relStep
#> [1] 6.055454e-06
#> 
#> $minAbsParApVar
#> [1] 0.05
#> 
#> $opt
#> [1] "nlminb"
#> 
#> $natural
#> [1] TRUE
#> 
#> $sigma
#> [1] 0
#> 
#> $optExpression
#> [1] TRUE
#> 
#> $literalFix
#> [1] TRUE
#> 
#> $sumProd
#> [1] FALSE
#> 
#> $rxControl
#> $scale
#> NULL
#> 
#> $method
#> liblsoda 
#>        2 
#> 
#> $atol
#> [1] 1e-04
#> 
#> $rtol
#> [1] 1e-04
#> 
#> $maxsteps
#> [1] 70000
#> 
#> $hmin
#> [1] 0
#> 
#> $hmax
#> [1] NA
#> 
#> $hini
#> [1] 0
#> 
#> $maxordn
#> [1] 12
#> 
#> $maxords
#> [1] 5
#> 
#> $covsInterpolation
#> locf 
#>    1 
#> 
#> $addCov
#> [1] TRUE
#> 
#> $returnType
#> rxSolve 
#>       0 
#> 
#> $sigma
#> NULL
#> 
#> $sigmaDf
#> NULL
#> 
#> $nCoresRV
#> [1] 1
#> 
#> $sigmaIsChol
#> [1] FALSE
#> 
#> $sigmaSeparation
#> [1] "auto"
#> 
#> $sigmaXform
#> identity 
#>        4 
#> 
#> $nDisplayProgress
#> [1] 10000
#> 
#> $amountUnits
#> [1] NA
#> 
#> $timeUnits
#> [1] "hours"
#> 
#> $addDosing
#> [1] FALSE
#> 
#> $stateTrim
#> [1] Inf
#> 
#> $updateObject
#> [1] FALSE
#> 
#> $omega
#> NULL
#> 
#> $omegaDf
#> NULL
#> 
#> $omegaIsChol
#> [1] FALSE
#> 
#> $omegaSeparation
#> [1] "auto"
#> 
#> $omegaXform
#> variance 
#>        6 
#> 
#> $nSub
#> [1] 1
#> 
#> $thetaMat
#> NULL
#> 
#> $thetaDf
#> NULL
#> 
#> $thetaIsChol
#> [1] FALSE
#> 
#> $nStud
#> [1] 1
#> 
#> $dfSub
#> [1] 0
#> 
#> $dfObs
#> [1] 0
#> 
#> $seed
#> NULL
#> 
#> $nsim
#> NULL
#> 
#> $minSS
#> [1] 10
#> 
#> $maxSS
#> [1] 10000
#> 
#> $strictSS
#> [1] 1
#> 
#> $infSSstep
#> [1] 12
#> 
#> $istateReset
#> [1] TRUE
#> 
#> $subsetNonmem
#> [1] TRUE
#> 
#> $hmaxSd
#> [1] 0
#> 
#> $maxAtolRtolFactor
#> [1] 0.1
#> 
#> $from
#> NULL
#> 
#> $to
#> NULL
#> 
#> $by
#> NULL
#> 
#> $length.out
#> NULL
#> 
#> $iCov
#> NULL
#> 
#> $keep
#> NULL
#> 
#> $keepF
#> character(0)
#> 
#> $drop
#> NULL
#> 
#> $warnDrop
#> [1] TRUE
#> 
#> $omegaLower
#> [1] -Inf
#> 
#> $omegaUpper
#> [1] Inf
#> 
#> $sigmaLower
#> [1] -Inf
#> 
#> $sigmaUpper
#> [1] Inf
#> 
#> $thetaLower
#> [1] -Inf
#> 
#> $thetaUpper
#> [1] Inf
#> 
#> $indLinPhiM
#> [1] 0
#> 
#> $indLinPhiTol
#> [1] 1e-07
#> 
#> $indLinMatExpType
#> expokit 
#>       2 
#> 
#> $indLinMatExpOrder
#> [1] 6
#> 
#> $idFactor
#> [1] TRUE
#> 
#> $mxhnil
#> [1] 0
#> 
#> $hmxi
#> [1] 0
#> 
#> $warnIdSort
#> [1] TRUE
#> 
#> $ssAtol
#> [1] 1e-08
#> 
#> $ssRtol
#> [1] 1e-06
#> 
#> $safeZero
#> [1] 1
#> 
#> $sumType
#> pairwise 
#>        1 
#> 
#> $prodType
#> long double 
#>           1 
#> 
#> $resample
#> NULL
#> 
#> $resampleID
#> [1] TRUE
#> 
#> $maxwhile
#> [1] 100000
#> 
#> $cores
#> [1] 0
#> 
#> $atolSens
#> [1] 1e-08
#> 
#> $rtolSens
#> [1] 1e-06
#> 
#> $ssAtolSens
#> [1] 1e-08
#> 
#> $ssRtolSens
#> [1] 1e-06
#> 
#> $simVariability
#> [1] NA
#> 
#> $nLlikAlloc
#> NULL
#> 
#> $useStdPow
#> [1] 0
#> 
#> $naTimeHandle
#> ignore 
#>      1 
#> 
#> $addlKeepsCov
#> [1] FALSE
#> 
#> $addlDropSs
#> [1] TRUE
#> 
#> $ssAtDoseTime
#> [1] TRUE
#> 
#> $ss2cancelAllPending
#> [1] FALSE
#> 
#> $naInterpolation
#> locf 
#>    1 
#> 
#> $keepInterpolation
#> na 
#>  2 
#> 
#> $safeLog
#> [1] 1
#> 
#> $safePow
#> [1] 1
#> 
#> $ssSolved
#> [1] TRUE
#> 
#> $linCmtSensType
#> auto 
#>  100 
#> 
#> $linCmtSensH
#> [1] 1e-04
#> 
#> $linCmtGillFtol
#> [1] 0
#> 
#> $linCmtGillK
#> [1] 20
#> 
#> $linCmtGillStep
#> [1] 4
#> 
#> $linCmtGillRtol
#> [1] 1.490116e-08
#> 
#> $linCmtShiErr
#> [1] 1.490116e-08
#> 
#> $linCmtShiMax
#> [1] 20
#> 
#> $linCmtScale
#> [1] 0 0 0 0 0 0 0
#> 
#> $linCmtHcmt
#> [1] 1
#> 
#> $linCmtHmeanI
#> geometric 
#>         2 
#> 
#> $linCmtHmeanO
#> geometric 
#>         2 
#> 
#> $linCmtSuspect
#> [1] 1e-06
#> 
#> $linCmtForwardMax
#> [1] 2
#> 
#> $.zeros
#> NULL
#> 
#> attr(,"class")
#> [1] "rxControl"
#> 
#> $method
#> [1] "ML"
#> 
#> $verbose
#> [1] TRUE
#> 
#> $returnNlme
#> [1] FALSE
#> 
#> $addProp
#> [1] "combined2"
#> 
#> $calcTables
#> [1] TRUE
#> 
#> $compress
#> [1] TRUE
#> 
#> $random
#> NULL
#> 
#> $fixed
#> NULL
#> 
#> $weights
#> NULL
#> 
#> $ci
#> [1] 0.95
#> 
#> $sigdig
#> [1] 4
#> 
#> $sigdigTable
#> [1] 4
#> 
#> $muRefCovAlg
#> [1] TRUE
#> 
#> $genRxControl
#> [1] TRUE
#> 
#> attr(,"class")
#> [1] "nlmeControl"
nlmixr2NlmeControl()
#> $maxIter
#> [1] 100
#> 
#> $pnlsMaxIter
#> [1] 100
#> 
#> $msMaxIter
#> [1] 100
#> 
#> $minScale
#> [1] 0.001
#> 
#> $tolerance
#> [1] 1e-05
#> 
#> $niterEM
#> [1] 25
#> 
#> $pnlsTol
#> [1] 0.001
#> 
#> $msTol
#> [1] 1e-06
#> 
#> $returnObject
#> [1] FALSE
#> 
#> $msVerbose
#> [1] FALSE
#> 
#> $msWarnNoConv
#> [1] TRUE
#> 
#> $gradHess
#> [1] TRUE
#> 
#> $apVar
#> [1] TRUE
#> 
#> $.relStep
#> [1] 6.055454e-06
#> 
#> $minAbsParApVar
#> [1] 0.05
#> 
#> $opt
#> [1] "nlminb"
#> 
#> $natural
#> [1] TRUE
#> 
#> $sigma
#> [1] 0
#> 
#> $optExpression
#> [1] TRUE
#> 
#> $literalFix
#> [1] TRUE
#> 
#> $sumProd
#> [1] FALSE
#> 
#> $rxControl
#> $scale
#> NULL
#> 
#> $method
#> liblsoda 
#>        2 
#> 
#> $atol
#> [1] 1e-04
#> 
#> $rtol
#> [1] 1e-04
#> 
#> $maxsteps
#> [1] 70000
#> 
#> $hmin
#> [1] 0
#> 
#> $hmax
#> [1] NA
#> 
#> $hini
#> [1] 0
#> 
#> $maxordn
#> [1] 12
#> 
#> $maxords
#> [1] 5
#> 
#> $covsInterpolation
#> locf 
#>    1 
#> 
#> $addCov
#> [1] TRUE
#> 
#> $returnType
#> rxSolve 
#>       0 
#> 
#> $sigma
#> NULL
#> 
#> $sigmaDf
#> NULL
#> 
#> $nCoresRV
#> [1] 1
#> 
#> $sigmaIsChol
#> [1] FALSE
#> 
#> $sigmaSeparation
#> [1] "auto"
#> 
#> $sigmaXform
#> identity 
#>        4 
#> 
#> $nDisplayProgress
#> [1] 10000
#> 
#> $amountUnits
#> [1] NA
#> 
#> $timeUnits
#> [1] "hours"
#> 
#> $addDosing
#> [1] FALSE
#> 
#> $stateTrim
#> [1] Inf
#> 
#> $updateObject
#> [1] FALSE
#> 
#> $omega
#> NULL
#> 
#> $omegaDf
#> NULL
#> 
#> $omegaIsChol
#> [1] FALSE
#> 
#> $omegaSeparation
#> [1] "auto"
#> 
#> $omegaXform
#> variance 
#>        6 
#> 
#> $nSub
#> [1] 1
#> 
#> $thetaMat
#> NULL
#> 
#> $thetaDf
#> NULL
#> 
#> $thetaIsChol
#> [1] FALSE
#> 
#> $nStud
#> [1] 1
#> 
#> $dfSub
#> [1] 0
#> 
#> $dfObs
#> [1] 0
#> 
#> $seed
#> NULL
#> 
#> $nsim
#> NULL
#> 
#> $minSS
#> [1] 10
#> 
#> $maxSS
#> [1] 10000
#> 
#> $strictSS
#> [1] 1
#> 
#> $infSSstep
#> [1] 12
#> 
#> $istateReset
#> [1] TRUE
#> 
#> $subsetNonmem
#> [1] TRUE
#> 
#> $hmaxSd
#> [1] 0
#> 
#> $maxAtolRtolFactor
#> [1] 0.1
#> 
#> $from
#> NULL
#> 
#> $to
#> NULL
#> 
#> $by
#> NULL
#> 
#> $length.out
#> NULL
#> 
#> $iCov
#> NULL
#> 
#> $keep
#> NULL
#> 
#> $keepF
#> character(0)
#> 
#> $drop
#> NULL
#> 
#> $warnDrop
#> [1] TRUE
#> 
#> $omegaLower
#> [1] -Inf
#> 
#> $omegaUpper
#> [1] Inf
#> 
#> $sigmaLower
#> [1] -Inf
#> 
#> $sigmaUpper
#> [1] Inf
#> 
#> $thetaLower
#> [1] -Inf
#> 
#> $thetaUpper
#> [1] Inf
#> 
#> $indLinPhiM
#> [1] 0
#> 
#> $indLinPhiTol
#> [1] 1e-07
#> 
#> $indLinMatExpType
#> expokit 
#>       2 
#> 
#> $indLinMatExpOrder
#> [1] 6
#> 
#> $idFactor
#> [1] TRUE
#> 
#> $mxhnil
#> [1] 0
#> 
#> $hmxi
#> [1] 0
#> 
#> $warnIdSort
#> [1] TRUE
#> 
#> $ssAtol
#> [1] 1e-08
#> 
#> $ssRtol
#> [1] 1e-06
#> 
#> $safeZero
#> [1] 1
#> 
#> $sumType
#> pairwise 
#>        1 
#> 
#> $prodType
#> long double 
#>           1 
#> 
#> $resample
#> NULL
#> 
#> $resampleID
#> [1] TRUE
#> 
#> $maxwhile
#> [1] 100000
#> 
#> $cores
#> [1] 0
#> 
#> $atolSens
#> [1] 1e-08
#> 
#> $rtolSens
#> [1] 1e-06
#> 
#> $ssAtolSens
#> [1] 1e-08
#> 
#> $ssRtolSens
#> [1] 1e-06
#> 
#> $simVariability
#> [1] NA
#> 
#> $nLlikAlloc
#> NULL
#> 
#> $useStdPow
#> [1] 0
#> 
#> $naTimeHandle
#> ignore 
#>      1 
#> 
#> $addlKeepsCov
#> [1] FALSE
#> 
#> $addlDropSs
#> [1] TRUE
#> 
#> $ssAtDoseTime
#> [1] TRUE
#> 
#> $ss2cancelAllPending
#> [1] FALSE
#> 
#> $naInterpolation
#> locf 
#>    1 
#> 
#> $keepInterpolation
#> na 
#>  2 
#> 
#> $safeLog
#> [1] 1
#> 
#> $safePow
#> [1] 1
#> 
#> $ssSolved
#> [1] TRUE
#> 
#> $linCmtSensType
#> auto 
#>  100 
#> 
#> $linCmtSensH
#> [1] 1e-04
#> 
#> $linCmtGillFtol
#> [1] 0
#> 
#> $linCmtGillK
#> [1] 20
#> 
#> $linCmtGillStep
#> [1] 4
#> 
#> $linCmtGillRtol
#> [1] 1.490116e-08
#> 
#> $linCmtShiErr
#> [1] 1.490116e-08
#> 
#> $linCmtShiMax
#> [1] 20
#> 
#> $linCmtScale
#> [1] 0 0 0 0 0 0 0
#> 
#> $linCmtHcmt
#> [1] 1
#> 
#> $linCmtHmeanI
#> geometric 
#>         2 
#> 
#> $linCmtHmeanO
#> geometric 
#>         2 
#> 
#> $linCmtSuspect
#> [1] 1e-06
#> 
#> $linCmtForwardMax
#> [1] 2
#> 
#> $.zeros
#> NULL
#> 
#> attr(,"class")
#> [1] "rxControl"
#> 
#> $method
#> [1] "ML"
#> 
#> $verbose
#> [1] TRUE
#> 
#> $returnNlme
#> [1] FALSE
#> 
#> $addProp
#> [1] "combined2"
#> 
#> $calcTables
#> [1] TRUE
#> 
#> $compress
#> [1] TRUE
#> 
#> $random
#> NULL
#> 
#> $fixed
#> NULL
#> 
#> $weights
#> NULL
#> 
#> $ci
#> [1] 0.95
#> 
#> $sigdig
#> [1] 4
#> 
#> $sigdigTable
#> [1] 4
#> 
#> $muRefCovAlg
#> [1] TRUE
#> 
#> $genRxControl
#> [1] TRUE
#> 
#> attr(,"class")
#> [1] "nlmeControl"