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Control for a PopED design task

Source: R/poped.R
popedControl.Rd

Control for a PopED design task

Usage

popedControl(
  stickyRecalcN = 4,
  maxOdeRecalc = 5,
  odeRecalcFactor = 10^(0.5),
  maxn = NULL,
  rxControl = NULL,
  sigdig = 4,
  important = NULL,
  unimportant = NULL,
  iFIMCalculationType = c("reduced", "full", "weighted", "loc", "reducedPFIM", "fullABC",
    "largeMat", "reducedFIMABC"),
  iApproximationMethod = c("fo", "foce", "focei", "foi"),
  iFOCENumInd = 1000,
  prior_fim = matrix(0, 0, 1),
  d_switch = c("d", "ed"),
  ofv_calc_type = c("lnD", "d", "a", "Ds", "inverse"),
  strEDPenaltyFile = "",
  ofv_fun = NULL,
  iEDCalculationType = c("mc", "laplace", "bfgs-laplace"),
  ED_samp_size = 45,
  bLHS = c("hypercube", "random"),
  bUseRandomSearch = TRUE,
  bUseStochasticGradient = TRUE,
  bUseLineSearch = TRUE,
  bUseExchangeAlgorithm = FALSE,
  bUseBFGSMinimizer = FALSE,
  bUseGrouped_xt = FALSE,
  EACriteria = c("modified", "fedorov"),
  strRunFile = "",
  poped_version = NULL,
  modtit = "PopED babelmixr2 model",
  output_file = "PopED_output_summary",
  output_function_file = "PopED_output_",
  strIterationFileName = "PopED_current.R",
  user_data = NULL,
  ourzero = 1e-05,
  dSeed = NULL,
  line_opta = NULL,
  line_optx = NULL,
  bShowGraphs = FALSE,
  use_logfile = FALSE,
  m1_switch = c("central", "complex", "analytic", "ad"),
  m2_switch = c("central", "complex", "analytic", "ad"),
  hle_switch = c("central", "complex", "ad"),
  gradff_switch = c("central", "complex", "analytic", "ad"),
  gradfg_switch = c("central", "complex", "analytic", "ad"),
  grad_all_switch = c("central", "complex"),
  rsit_output = 5,
  sgit_output = 1,
  hm1 = 1e-05,
  hlf = 1e-05,
  hlg = 1e-05,
  hm2 = 1e-05,
  hgd = 1e-05,
  hle = 1e-05,
  AbsTol = 1e-06,
  RelTol = 1e-06,
  iDiffSolverMethod = NULL,
  bUseMemorySolver = FALSE,
  rsit = 300,
  sgit = 150,
  intrsit = 250,
  intsgit = 50,
  maxrsnullit = 50,
  convergence_eps = 1e-08,
  rslxt = 10,
  rsla = 10,
  cfaxt = 0.001,
  cfaa = 0.001,
  bGreedyGroupOpt = FALSE,
  EAStepSize = 0.01,
  EANumPoints = FALSE,
  EAConvergenceCriteria = 1e-20,
  bEANoReplicates = FALSE,
  BFGSProjectedGradientTol = 1e-04,
  BFGSTolerancef = 0.001,
  BFGSToleranceg = 0.9,
  BFGSTolerancex = 0.1,
  ED_diff_it = 30,
  ED_diff_percent = 10,
  line_search_it = 50,
  Doptim_iter = 1,
  iCompileOption = c("none", "full", "mcc", "mpi"),
  compileOnly = FALSE,
  iUseParallelMethod = c("mpi", "matlab"),
  MCC_Dep = NULL,
  strExecuteName = "calc_fim.exe",
  iNumProcesses = 2,
  iNumChunkDesignEvals = -2,
  Mat_Out_Pre = "parallel_output",
  strExtraRunOptions = "",
  dPollResultTime = 0.1,
  strFunctionInputName = "function_input",
  bParallelRS = FALSE,
  bParallelSG = FALSE,
  bParallelMFEA = FALSE,
  bParallelLS = FALSE,
  groupsize = NULL,
  time = "time",
  timeLow = "low",
  timeHi = "high",
  id = "id",
  m = NULL,
  x = NULL,
  ni = NULL,
  maxni = NULL,
  minni = NULL,
  maxtotni = NULL,
  mintotni = NULL,
  maxgroupsize = NULL,
  mingroupsize = NULL,
  maxtotgroupsize = NULL,
  mintotgroupsize = NULL,
  xt_space = NULL,
  a = NULL,
  maxa = NULL,
  mina = NULL,
  a_space = NULL,
  x_space = NULL,
  use_grouped_xt = FALSE,
  grouped_xt = NULL,
  use_grouped_a = FALSE,
  grouped_a = NULL,
  use_grouped_x = FALSE,
  grouped_x = NULL,
  our_zero = NULL,
  auto_pointer = "",
  user_distribution_pointer = "",
  minxt = NULL,
  maxxt = NULL,
  discrete_xt = NULL,
  discrete_a = NULL,
  fixRes = FALSE,
  script = NULL,
  overwrite = TRUE,
  literalFix = TRUE,
  opt_xt = FALSE,
  opt_a = FALSE,
  opt_x = FALSE,
  opt_samps = FALSE,
  optTime = TRUE,
  ...
)

Arguments

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

maxn

Maximum number of design points for optimization; By default this is declared by the maximum number of design points in the babelmixr2 dataset (when NULL)

rxControl

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

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)

important

character vector of important parameters or NULL for default. This is used with Ds-optimality

unimportant

character vector of unimportant parameters or NULL for default. This is used with Ds-optimality

iFIMCalculationType

can be either an integer or a named value of the Fisher Information Matrix type:

  • 0/"full" = Full FIM

  • 1/"reduced" = Reduced FIM

  • 2/"weighted" = weighted models

  • 3/"loc" = Loc models

  • 4/"reducedPFIM" = reduced FIM with derivative of SD of sigma as in PFIM

  • 5/"fullABC" = FULL FIM parameterized with A,B,C matrices & derivative of variance

  • 6/"largeMat" = Calculate one model switch at a time, good for large matrices

  • 7/"reducedFIMABC" = =Reduced FIM parameterized with A,B,C matrices & derivative of variance

iApproximationMethod

Approximation method for model, 0=FO, 1=FOCE, 2=FOCEI, 3=FOI

iFOCENumInd

integer; number of individuals in focei solve

prior_fim

matrix; prior FIM

d_switch

integer or character option:

  • 0/"ed" = ED design

  • 1/"d" = D design

ofv_calc_type

objective calculation type:

  • 1/"d" = D-optimality". Determinant of the FIM: det(FIM)

  • 2/"a" = "A-optimality". Inverse of the sum of the expected parameter variances: 1/trace_matrix(inv(FIM))

  • 4/"lnD" = "lnD-optimality". Natural logarithm of the determinant of the FIM: log(det(FIM))

  • 6/"Ds" = "Ds-optimality". Ratio of the Determinant of the FIM and the Determinant of the uninteresting rows and columns of the FIM: det(FIM)/det(FIM_u)

  • 7/"inverse" = Inverse of the sum of the expected parameter RSE: 1/sum(get_rse(FIM,poped.db,use_percent=FALSE))

strEDPenaltyFile

Penalty function name or path and filename, empty string means no penalty. User defined criterion can be defined this way.

ofv_fun

User defined function used to compute the objective function. The function must have a poped database object as its first argument and have "..." in its argument list. Can be referenced as a function or as a file name where the function defined in the file has the same name as the file. e.g. "cost.txt" has a function named "cost" in it.

iEDCalculationType

ED Integral Calculation type:

  • 0/"mc" = Monte-Carlo-Integration

  • 1/"laplace" = Laplace Approximation

  • 2/"bfgs-laplace" = BFGS Laplace Approximation

ED_samp_size

Sample size for E-family sampling

bLHS

How to sample from distributions in E-family calculations. 0=Random Sampling, 1=LatinHyperCube –

bUseRandomSearch

  • ******START OF Optimization algorithm SPECIFICATION OPTIONS**********

Use random search (1=TRUE, 0=FALSE)

bUseStochasticGradient

Use Stochastic Gradient search (1=TRUE, 0=FALSE)

bUseLineSearch

Use Line search (1=TRUE, 0=FALSE)

bUseExchangeAlgorithm

Use Exchange algorithm (1=TRUE, 0=FALSE)

bUseBFGSMinimizer

Use BFGS Minimizer (1=TRUE, 0=FALSE)

bUseGrouped_xt

Use grouped time points (1=TRUE, 0=FALSE).

EACriteria

Exchange Algorithm Criteria:

  • 1/"modified" = Modified

  • 2/"fedorov" = Fedorov

strRunFile

Filename and path, or function name, for a run file that is used instead of the regular PopED call.

poped_version

  • ******START OF Labeling and file names SPECIFICATION OPTIONS**********

The current PopED version

modtit

The model title

output_file

Filename and path of the output file during search

output_function_file

Filename suffix of the result function file

strIterationFileName

Filename and path for storage of current optimal design

user_data

  • ******START OF Miscellaneous SPECIFICATION OPTIONS**********

User defined data structure that, for example could be used to send in data to the model

ourzero

Value to interpret as zero in design

dSeed

The seed number used for optimization and sampling – integer or -1 which creates a random seed as.integer(Sys.time()) or NULL.

line_opta

Vector for line search on continuous design variables (1=TRUE,0=FALSE)

line_optx

Vector for line search on discrete design variables (1=TRUE,0=FALSE)

bShowGraphs

Use graph output during search

use_logfile

If a log file should be used (0=FALSE, 1=TRUE)

m1_switch

Method used to calculate M1:

  • 1/"central" = Central difference

  • 0/"complex" = Complex difference

  • 20/"analytic" = Analytic derivative

  • 30/"ad" = Automatic differentiation

m2_switch

Method used to calculate M2:

  • 1/"central" = Central difference

  • 0/"complex" = Complex difference

  • 20/"analytic" = Analytic derivative

  • 30/"ad" = Automatic differentiation

hle_switch

Method used to calculate linearization of residual error:

  • 1/"central" = Central difference

  • 0/"complex" = Complex difference

  • 30/"ad" = Automatic differentiation

gradff_switch

Method used to calculate the gradient of the model:

  • 1/"central" = Central difference

  • 0/"complex" = Complex difference

  • 20/"analytic" = Analytic derivative

  • 30/"ad" = Automatic differentiation

gradfg_switch

Method used to calculate the gradient of the parameter vector g:

  • 1/"central" = Central difference

  • 0/"complex" = Complex difference

  • 20/"analytic" = Analytic derivative

  • 30/"ad" = Automatic differentiation

grad_all_switch

Method used to calculate all the gradients:

  • 1/"central" = Central difference

  • 0/"complex" = Complex difference

rsit_output

Number of iterations in random search between screen output

sgit_output

Number of iterations in stochastic gradient search between screen output

hm1

Step length of derivative of linearized model w.r.t. typical values

hlf

Step length of derivative of model w.r.t. g

hlg

Step length of derivative of g w.r.t. b

hm2

Step length of derivative of variance w.r.t. typical values

hgd

Step length of derivative of OFV w.r.t. time

hle

Step length of derivative of model w.r.t. sigma

AbsTol

The absolute tolerance for the diff equation solver

RelTol

The relative tolerance for the diff equation solver

iDiffSolverMethod

The diff equation solver method, NULL as default.

bUseMemorySolver

If the differential equation results should be stored in memory (1) or not (0)

rsit

Number of Random search iterations

sgit

Number of stochastic gradient iterations

intrsit

Number of Random search iterations with discrete optimization.

intsgit

Number of Stochastic Gradient search iterations with discrete optimization

maxrsnullit

Iterations until adaptive narrowing in random search

convergence_eps

Stochastic Gradient convergence value, (difference in OFV for D-optimal, difference in gradient for ED-optimal)

rslxt

Random search locality factor for sample times

rsla

Random search locality factor for covariates

cfaxt

Stochastic Gradient search first step factor for sample times

cfaa

Stochastic Gradient search first step factor for covariates

bGreedyGroupOpt

Use greedy algorithm for group assignment optimization

EAStepSize

Exchange Algorithm StepSize

EANumPoints

Exchange Algorithm NumPoints

EAConvergenceCriteria

Exchange Algorithm Convergence Limit/Criteria

bEANoReplicates

Avoid replicate samples when using Exchange Algorithm

BFGSProjectedGradientTol

BFGS Minimizer Convergence Criteria Normalized Projected Gradient Tolerance

BFGSTolerancef

BFGS Minimizer Line Search Tolerance f

BFGSToleranceg

BFGS Minimizer Line Search Tolerance g

BFGSTolerancex

BFGS Minimizer Line Search Tolerance x

ED_diff_it

Number of iterations in ED-optimal design to calculate convergence criteria

ED_diff_percent

ED-optimal design convergence criteria in percent

line_search_it

Number of grid points in the line search

Doptim_iter

Number of iterations of full Random search and full Stochastic Gradient if line search is not used

iCompileOption

Compile options for PopED

  • "none"/-1 = No compilation

  • "full/0 or 3 = Full compilation

  • "mcc"/1 or 4 = Only using MCC (shared lib)

  • "mpi"/2 or 5 = Only MPI,

When using numbers, option 0,1,2 runs PopED and option 3,4,5 stops after compilation.

When using characters, the option compileOnly determines if the model is only compiled (and PopED is not run).

compileOnly

logical; only compile the model, do not run PopED (in conjunction with iCompileOption)

iUseParallelMethod

Parallel method to use

  • 0/"matlab"= Matlab PCT

  • 1/"mpi" = MPI

MCC_Dep

Additional dependencies used in MCC compilation (mat-files), if several space separated

strExecuteName

Compilation output executable name

iNumProcesses

Number of processes to use when running in parallel (e.g. 3 = 2 workers, 1 job manager)

iNumChunkDesignEvals

Number of design evaluations that should be evaluated in each process before getting new work from job manager

Mat_Out_Pre

The prefix of the output mat file to communicate with the executable

strExtraRunOptions

Extra options send to e$g. the MPI executable or a batch script, see execute_parallel$m for more information and options

dPollResultTime

Polling time to check if the parallel execution is finished

strFunctionInputName

The file containing the popedInput structure that should be used to evaluate the designs

bParallelRS

If the random search is going to be executed in parallel

bParallelSG

If the stochastic gradient search is going to be executed in parallel

bParallelMFEA

If the modified exchange algorithm is going to be executed in parallel

bParallelLS

If the line search is going to be executed in parallel

groupsize

Vector defining the size of the different groups (num individuals in each group). If only one number then the number will be the same in every group.

time

string that represents the time in the dataset (ie xt)

timeLow

string that represents the lower design time (ie minxt)

timeHi

string that represents the upper design time (ie maxmt)

id

The id variable

m

Number of groups in the study. Each individual in a group will have the same design.

x

A matrix defining the initial discrete values for the model Each row is a group/individual.

ni

Vector defining the number of samples for each group.

maxni

  • ******START OF DESIGN SPACE OPTIONS**********

Max number of samples per group/individual

minni

Min number of samples per group/individual

maxtotni

Number defining the maximum number of samples allowed in the experiment.

mintotni

Number defining the minimum number of samples allowed in the experiment.

maxgroupsize

Vector defining the max size of the different groups (max number of individuals in each group)

mingroupsize

Vector defining the min size of the different groups (min num individuals in each group) –

maxtotgroupsize

The total maximal groupsize over all groups

mintotgroupsize

The total minimal groupsize over all groups

xt_space

Cell array cell defining the discrete variables allowed for each xt value. Can also be a vector of values c(1:10) (same values allowed for all xt), or a list of lists list(1:10, 2:23, 4:6) (one for each value in xt in row major order or just for one row in xt, and all other rows will be duplicated).

a

Matrix defining the initial continuous covariate values. n_rows=number of groups, n_cols=number of covariates. If the number of rows is one and the number of groups > 1 then all groups are assigned the same values.

maxa

Vector defining the max value for each covariate. If a single value is supplied then all a values are given the same max value

mina

Vector defining the min value for each covariate. If a single value is supplied then all a values are given the same max value

a_space

Cell array cell defining the discrete variables allowed for each a value. Can also be a list of values list(1:10) (same values allowed for all a), or a list of lists list(1:10, 2:23, 4:6) (one for each value in a).

x_space

Cell array cell defining the discrete variables for each x value.

use_grouped_xt

Group sampling times between groups so that each group has the same values (TRUE or FALSE).

grouped_xt

Matrix defining the grouping of sample points. Matching integers mean that the points are matched. Allows for finer control than use_grouped_xt

use_grouped_a

Group continuous design variables between groups so that each group has the same values (TRUE or FALSE).

grouped_a

Matrix defining the grouping of continuous design variables. Matching integers mean that the values are matched. Allows for finer control than use_grouped_a.

use_grouped_x

Group discrete design variables between groups so that each group has the same values (TRUE or FALSE).

grouped_x

Matrix defining the grouping of discrete design variables. Matching integers mean that the values are matched. Allows for finer control than use_grouped_x.

our_zero

Value to interpret as zero in design.

auto_pointer

Filename and path, or function name, for the Autocorrelation function, empty string means no autocorrelation

user_distribution_pointer

Filename and path, or function name, for user defined distributions for E-family designs

minxt

Matrix or single value defining the minimum value for each xt sample. If a single value is supplied then all xt values are given the same minimum value

maxxt

Matrix or single value defining the maximum value for each xt sample. If a single value is supplied then all xt values are given the same maximum value.

discrete_xt

Cell array cell defining the discrete variables allowed for each xt value. Can also be a list of values list(1:10) (same values allowed for all xt), or a list of lists list(1:10, 2:23, 4:6) (one for each value in xt). See examples in create_design_space.

discrete_a

Cell array cell defining the discrete variables allowed for each a value. Can also be a list of values list(1:10) (same values allowed for all a), or a list of lists list(1:10, 2:23, 4:6) (one for each value in a). See examples in create_design_space.

fixRes

boolean; Fix the residuals to what is specified by the model

script

write a PopED/rxode2 script that can be modified for more fine control. The default is NULL.

When script is TRUE, the script is returned as a lines that would be written to a file and with the class babelmixr2popedScript. This allows it to be printed as the script on screen.

When script is a file name (with an R extension), the script is written to that file.

overwrite

[logical(1)]
If TRUE, an existing file in place is allowed if it it is both readable and writable. Default is FALSE.

literalFix

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

opt_xt

boolean to indicate if this is meant for optimizing times

opt_a

boolean to indicate if this is meant for optimizing covariates

opt_x

boolean to indicate if the discrete design variables be optimized

opt_samps

boolean to indicate if the sample optimizer is used (not implemented yet in PopED)

optTime

boolean to indicate if the global time indexer inside of babelmixr2 is reset if the times are different. By default this is TRUE. If FALSE you can get slightly better run times and possibly slightly different results. When optTime is FALSE the global indexer is reset every time the PopED rxode2 is setup for a problem or when a poped dataset is created. You can manually reset with popedMultipleEndpointResetTimeIndex()

...

other parameters for PopED control

Value

popedControl object

Author

Matthew L. Fidler