Control Options for dynmodel
dynmodelControl(
...,
ci = 0.95,
nlmixrOutput = FALSE,
digs = 3,
lower = Inf,
upper = Inf,
method = c("bobyqa", "NelderMead", "lbfgsb3c", "LBFGSB", "PORT", "mma",
"lbfgsbLG", "slsqp", "Rvmmin"),
maxeval = 999,
scaleTo = 1,
scaleObjective = 0,
normType = c("rescale2", "constant", "mean", "rescale", "std", "len"),
scaleType = c("nlmixr", "norm", "mult", "multAdd"),
scaleCmax = 1e+05,
scaleCmin = 1e05,
scaleC = NULL,
scaleC0 = 1e+05,
atol = NULL,
rtol = NULL,
ssAtol = NULL,
ssRtol = NULL,
npt = NULL,
rhobeg = 0.2,
rhoend = NULL,
iprint = 0,
print = 1,
maxfun = NULL,
trace = 0,
factr = NULL,
pgtol = NULL,
abstol = NULL,
reltol = NULL,
lmm = NULL,
maxit = 100000L,
eval.max = NULL,
iter.max = NULL,
abs.tol = NULL,
rel.tol = NULL,
x.tol = NULL,
xf.tol = NULL,
step.min = NULL,
step.max = NULL,
sing.tol = NULL,
scale.init = NULL,
diff.g = NULL,
boundTol = NULL,
epsilon = NULL,
derivSwitchTol = NULL,
sigdig = 4,
covMethod = c("nlmixrHess", "optimHess"),
gillK = 10L,
gillStep = 4,
gillFtol = 0,
gillRtol = sqrt(.Machine$double.eps),
gillKcov = 10L,
gillStepCov = 2,
gillFtolCov = 0,
rxControl = NULL
)
...  Other arguments including scaling factors for each compartment. This includes S# = numeric will scale a compartment # by a dividing the compartment amount by the scale factor, like NONMEM. 

ci  Confidence level for some tables. By default this is 0.95 or 95% confidence. 
nlmixrOutput  Option to change output style to nlmixr output. By default this is FALSE. 
digs  Option for the number of significant digits of the output. By default this is 3. 
lower  Lower bounds on the parameters used in optimization. By default this is Inf. 
upper  Upper bounds on the parameters used in optimization. By default this is Inf. 
method  The method for solving ODEs. Currently this supports:

maxeval  Maximum number of iterations for NelderMead of simplex search. By default this is 999. 
scaleTo  Scale the initial parameter estimate to this value. By default this is 1. When zero or below, no scaling is performed. 
scaleObjective  Scale the initial objective function to this value. By default this is 1. 
normType  This is the type of parameter
normalization/scaling used to get the scaled initial values
for nlmixr. These are used with With the exception of In general, all all scaling formula can be described by: v_scaled = (v_unscaledC_1)/C_2 Where The other data normalization approaches follow the following formula v_scaled = (v_unscaledC_1)/C_2;

scaleType  The scaling scheme for nlmixr. The supported types are:

scaleCmax  Maximum value of the scaleC to prevent overflow. 
scaleCmin  Minimum value of the scaleC to prevent underflow. 
scaleC  The scaling constant used with
These parameter scaling coefficients are chose to try to keep similar slopes among parameters. That is they all follow the slopes approximately on a logscale. While these are chosen in a logical manner, they may not always apply. You can specify each parameters scaling factor by this parameter if you wish. 
scaleC0  Number to adjust the scaling factor by if the initial gradient is zero. 
atol  a numeric absolute tolerance (1e8 by default) used by the ODE solver to determine if a good solution has been achieved; This is also used in the solved linear model to check if prior doses do not add anything to the solution. 
rtol  a numeric relative tolerance ( 
ssAtol  Steady state atol convergence factor. Can be a vector based on each state. 
ssRtol  Steady state rtol convergence factor. Can be a vector based on each state. 
npt  The number of points used to approximate the objective function via a quadratic approximation for bobyqa. The value of npt must be in the interval [n+2,(n+1)(n+2)/2] where n is the number of parameters in par. Choices that exceed 2*n+1 are not recommended. If not defined, it will be set to 2*n + 1 
rhobeg  Beginning change in parameters for bobyqa algorithm (trust region). By default this is 0.2 or 20 parameters when the parameters are scaled to 1. rhobeg and rhoend must be set to the initial and final values of a trust region radius, so both must be positive with 0 < rhoend < rhobeg. Typically rhobeg should be about one tenth of the greatest expected change to a variable. Note also that smallest difference abs(upperlower) should be greater than or equal to rhobeg*2. If this is not the case then rhobeg will be adjusted. 
rhoend  The smallest value of the trust region radius that is allowed. If not defined, then 10^(sigdig1) will be used. 
iprint  Print option for optimization. See 
Integer representing when the outer step is printed. When this is 0 or do not print the iterations. 1 is print every function evaluation (default), 5 is print every 5 evaluations. 

maxfun  The maximum allowed number of function evaluations. If this is
exceeded, the method will terminate. See 
trace  Tracing information on the progress of the optimization is
produced. See 
factr  Controls the convergence of the "LBFGSB" method. Convergence
occurs when the reduction in the objective is within this factor of the
machine tolerance. Default is 1e10, which gives a tolerance of about

pgtol  is a double precision variable. On entry pgtol >= 0 is specified by the user. The iteration will stop when:
where pg_i is the ith component of the projected gradient. On exit pgtol is unchanged. This defaults to zero, when the check is suppressed. 
abstol  Absolute tolerance for nlmixr optimizer 
reltol  tolerance for nlmixr 
lmm  An integer giving the number of BFGS updates retained in the "LBFGSB" method, It defaults to 7. 
maxit  Maximum number of iterations for lbfgsb3c. See

eval.max  Number of maximum evaluations of the objective function 
iter.max  Maximum number of iterations allowed. 
abs.tol  Used in NelderMead optimization and PORT optimization. Absolute tolerance. Defaults to 0 so the absolute convergence test is not used. If the objective function is known to be nonnegative, the previous default of 1e20 would be more appropriate. 
rel.tol  Relative tolerance before nlminb stops. 
x.tol  X tolerance for nlmixr optimizers 
xf.tol  Used in NelderMead optimization and PORT optimization. false
convergence tolerance. Defaults to 2.2e14. See 
step.min  Used in NelderMead optimization and PORT optimization.
Minimum step size. By default this is 1. See 
step.max  Used in NelderMead optimization and PORT optimization.
Maximum step size. By default this is 1. See 
sing.tol  Used in NelderMead optimization and PORT optimization.
Singular convergence tolerance; defaults to rel.tol. See

scale.init  Used in NelderMead optimization and PORT optimization.
See 
diff.g  Used in NelderMead optimization and PORT optimization. An
estimated bound on the relative error in the objective function value. See

boundTol  Tolerance for boundary issues. 
epsilon  Precision of estimate for n1qn1 optimization. 
derivSwitchTol  The tolerance to switch forward to central differences. 
sigdig  Optimization significant digits. This controls:

covMethod  Method for calculating covariance. In this discussion, R is the Hessian matrix of the objective function. The S matrix is the sum of individual gradient crossproduct (evaluated at the individual empirical Bayes estimates). 
gillK  The total number of possible steps to determine the optimal forward/central difference step size per parameter (by the Gill 1983 method). If 0, no optimal step size is determined. Otherwise this is the optimal step size determined. 
gillStep  When looking for the optimal forward difference step size, this is This is the step size to increase the initial estimate by. So each iteration the new step size = (prior step size)*gillStep 
gillFtol  The gillFtol is the gradient error tolerance that is acceptable before issuing a warning/error about the gradient estimates. 
gillRtol  The relative tolerance used for Gill 1983 determination of optimal step size. 
gillKcov  The total number of possible steps to determine the optimal forward/central difference step size per parameter (by the Gill 1983 method) during the covariance step. If 0, no optimal step size is determined. Otherwise this is the optimal step size determined. 
gillStepCov  When looking for the optimal forward difference step size, this is This is the step size to increase the initial estimate by. So each iteration during the covariance step is equal to the new step size = (prior step size)*gillStepCov 
gillFtolCov  The gillFtol is the gradient error tolerance that is acceptable before issuing a warning/error about the gradient estimates during the covariance step. 
rxControl  This uses RxODE family of objects, file, or model
specification to solve a ODE system. See 
dynmodelControl list for options during dynmodel optimization
Mason McComb and Matthew L. Fidler