nlmixr2NlmeControl  R Documentation 
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.
nlmixr2NlmeControl(
maxIter = 100,
pnlsMaxIter = 100,
msMaxIter = 100,
minScale = 0.001,
tolerance = 1e05,
niterEM = 25,
pnlsTol = 0.001,
msTol = 1e06,
returnObject = FALSE,
msVerbose = FALSE,
msWarnNoConv = TRUE,
gradHess = TRUE,
apVar = TRUE,
.relStep = .Machine$double.eps^(1/3),
minAbsParApVar = 0.05,
opt = c("nlminb", "nlm"),
natural = TRUE,
sigma = NULL,
optExpression = TRUE,
literalFix = TRUE,
sumProd = FALSE,
rxControl = NULL,
method = c("ML", "REML"),
random = NULL,
fixed = NULL,
weights = NULL,
verbose = TRUE,
returnNlme = FALSE,
addProp = c("combined2", "combined1"),
calcTables = TRUE,
compress = TRUE,
adjObf = TRUE,
ci = 0.95,
sigdig = 4,
sigdigTable = NULL,
muRefCovAlg = TRUE,
...
)
nlmeControl(
maxIter = 100,
pnlsMaxIter = 100,
msMaxIter = 100,
minScale = 0.001,
tolerance = 1e05,
niterEM = 25,
pnlsTol = 0.001,
msTol = 1e06,
returnObject = FALSE,
msVerbose = FALSE,
msWarnNoConv = TRUE,
gradHess = TRUE,
apVar = TRUE,
.relStep = .Machine$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,
...
)
maxIter 
maximum number of iterations for the 
pnlsMaxIter 
maximum number of iterations
for the 
msMaxIter 
maximum number of iterations for 
minScale 
minimum factor by which to shrink the default step size
in an attempt to decrease the sum of squares in the 
tolerance 
tolerance for the convergence criterion in the

niterEM 
number of iterations for the EM algorithm used to refine the initial estimates of the random effects variancecovariance coefficients. Default is 25. 
pnlsTol 
tolerance for the convergence criterion in 
msTol 
tolerance for the convergence criterion in 
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

msVerbose 
a logical value passed as the 
msWarnNoConv 
logical indicating if a 
gradHess 
a logical value indicating whether numerical gradient
vectors and Hessian matrices of the loglikelihood function should
be used in the 
apVar 
a logical value indicating whether the approximate
covariance matrix of the variancecovariance parameters should be
calculated. Default is 
.relStep 
relative step for numerical derivatives
calculations. Default is 
minAbsParApVar 
numeric value  minimum absolute parameter value
in the approximate variance calculation. The default is 
opt 
the optimizer to be used, either 
natural 
a logical value indicating whether the 
sigma 
optionally a positive number to fix the residual error at.
If 
optExpression 
Optimize the rxode2 expression to speed up calculation. By default this is turned on. 
literalFix 
boolean, substitute fixed population values as literals and readjust 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 
rxControl 
'rxode2' ODE solving options during fitting, created with 'rxControl()' 
method 
a character string. If 
random 
optionally, any of the following: (i) a twosided formula
of the form 
fixed 
a twosided linear formula of the form

weights 
an optional 
verbose 
an optional logical value. If 
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:
The combined2 error model can be described by the following equation:
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 
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 
ci 
Confidence level for some tables. By default this is 0.95 or 95% confidence. 
sigdig 
Optimization significant digits. This controls:

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 mureferenced are treated as mureferenced covariates by: 1. Creating a internal datavariable 'nlmixrMuDerCov#' for each algebraic mureferenced expression 2. Change the algebraic expression to 'nlmixrMuDerCov# * mu_cov_theta' 3. Use the internal mureferenced 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

a nlmixrnlme list
Other Estimation control:
foceiControl()
,
saemControl()
nlmeControl()
nlmixr2NlmeControl()
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