saemControl | R Documentation |

Control Options for SAEM

saemControl( seed = 99, nBurn = 200, nEm = 300, nmc = 3, nu = c(2, 2, 2), print = 1, trace = 0, covMethod = c("linFim", "fim", "r,s", "r", "s", ""), calcTables = TRUE, logLik = FALSE, nnodesGq = 3, nsdGq = 1.6, optExpression = TRUE, adjObf = TRUE, sumProd = FALSE, addProp = c("combined2", "combined1"), tol = 1e-06, itmax = 30, type = c("nelder-mead", "newuoa"), powRange = 10, lambdaRange = 3, odeRecalcFactor = 10^(0.5), maxOdeRecalc = 5L, perSa = 0.75, perNoCor = 0.75, perFixOmega = 0.1, perFixResid = 0.1, compress = TRUE, rxControl = NULL, sigdig = NULL, sigdigTable = NULL, ci = 0.95, muRefCov = TRUE, ... )

`seed` |
Random Seed for SAEM step. (Needs to be set for reproducibility.) By default this is 99. |

`nBurn` |
Number of iterations in the first phase, ie the MCMC/Stochastic Approximation
steps. This is equivalent to Monolix's |

`nEm` |
Number of iterations in the Expectation-Maximization
(EM) Step. This is equivalent to Monolix's |

`nmc` |
Number of Markov Chains. By default this is 3. When
you increase the number of chains the numerical integration by
MC method will be more accurate at the cost of more
computation. In Monolix this is equivalent to |

`nu` |
This is a vector of 3 integers. They represent the
numbers of transitions of the three different kernels used in
the Hasting-Metropolis algorithm. The default value is The first value represents the initial number of multi-variate Gibbs samples are taken from a normal distribution. The second value represents the number of uni-variate, or multi- dimensional random walk Gibbs samples are taken. The third value represents the number of bootstrap/reshuffling or uni-dimensional random samples are taken. |

`print` |
The number it iterations that are completed before anything is printed to the console. By default, this is 1. |

`trace` |
An integer indicating if you want to trace(1) the SAEM algorithm process. Useful for debugging, but not for typical fitting. |

`covMethod` |
Method for calculating covariance. In this discussion, R is the Hessian matrix of the objective function. The S matrix is the sum of each individual's gradient cross-product (evaluated at the individual empirical Bayes estimates). " " " " " "" Does not calculate the covariance step. |

`calcTables` |
This boolean is to determine if the foceiFit
will calculate tables. By default this is |

`logLik` |
boolean indicating that log-likelihood should be calculate by Gaussian quadrature. |

`nnodesGq` |
number of nodes to use for the Gaussian quadrature when computing the likelihood with this method (defaults to 1, equivalent to the Laplaclian likelihood) |

`nsdGq` |
span (in SD) over which to integrate when computing the likelihood by Gaussian quadrature. Defaults to 3 (eg 3 times the SD) |

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

`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 |

`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 |

`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*f^c)*err The combined2 error model can be described by the following equation: y = f + sqrt(a^2 + b^2*(f^c)^2)*err 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) |

`tol` |
This is the tolerance for the regression models used for complex residual errors (ie add+prop etc) |

`itmax` |
This is the maximum number of iterations for the regression models used for complex residual errors. The number of iterations is itmax*number of parameters |

`type` |
indicates the type of optimization for the residuals; Can be one of c("nelder-mead", "newuoa") |

`powRange` |
This indicates the range that powers can take for residual errors; By default this is 10 indicating the range is c(-10, 10) |

`lambdaRange` |
This indicates the range that Box-Cox and Yeo-Johnson parameters are constrained to be; The default is 3 indicating the range c(-3,3) |

`odeRecalcFactor` |
The ODE recalculation factor when ODE solving goes bad, this is the factor the rtol/atol is reduced |

`maxOdeRecalc` |
Maximum number of times to reduce the ODE tolerances and try to resolve the system if there was a bad ODE solve. |

`perSa` |
This is the percent of the time the 'nBurn' iterations in phase runs runs a simulated annealing. |

`perNoCor` |
This is the percentage of the MCMC phase of the SAEM algorithm where the variance/covariance matrix has no correlations. By default this is 0.75 or 75 Monte-carlo iteration. |

`perFixOmega` |
This is the percentage of the 'nBurn' phase where the omega values are unfixed to allow better exploration of the likelihood surface. After this time, the omegas are fixed during optimization. |

`perFixResid` |
This is the percentage of the 'nBurn' phase where the residual components are unfixed to allow better exploration of the likelihood surface. |

`compress` |
Should the object have compressed items |

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

`sigdig` |
Specifies the "significant digits" that the ode
solving requests. When specified this controls the relative and
absolute tolerances of the ODE solvers. By default the tolerance
is |

`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. |

`ci` |
Confidence level for some tables. By default this is 0.95 or 95% confidence. |

`muRefCov` |
This controls if mu-referenced covariates in 'saem' are handled differently than non mu-referenced covariates. When 'TRUE', mu-referenced covariates have special handling. When 'FALSE' mu-referenced covariates are treated the same as any other input parameter. |

`...` |
Other arguments to control SAEM. |

List of options to be used in `nlmixr2`

fit for
SAEM.

Wenping Wang & Matthew L. Fidler

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