Control for a PopED design task
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,
...
)popedControl object
The number of bad ODE solves before reducing the atol/rtol for the rest of the problem.
Maximum number of times to reduce the ODE tolerances and try to resolve the system if there was a bad ODE solve.
The ODE recalculation factor when ODE solving goes bad, this is the factor the rtol/atol is reduced
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)
`rxode2` ODE solving options during fitting, created with `rxControl()`
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)
character vector of important parameters or NULL for default. This is used with Ds-optimality
character vector of unimportant parameters or NULL for default. This is used with Ds-optimality
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
Approximation method for model, 0=FO, 1=FOCE, 2=FOCEI, 3=FOI
integer; number of individuals in focei solve
matrix; prior FIM
integer or character option:
0/"ed" = ED design
1/"d" = D design
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))
Penalty function name or path and filename, empty string means no penalty. User defined criterion can be defined this way.
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.
ED Integral Calculation type:
0/"mc" = Monte-Carlo-Integration
1/"laplace" = Laplace Approximation
2/"bfgs-laplace" = BFGS Laplace Approximation
Sample size for E-family sampling
How to sample from distributions in E-family calculations. 0=Random Sampling, 1=LatinHyperCube --
******START OF Optimization algorithm SPECIFICATION OPTIONS**********
Use random search (1=TRUE, 0=FALSE)
Use Stochastic Gradient search (1=TRUE, 0=FALSE)
Use Line search (1=TRUE, 0=FALSE)
Use Exchange algorithm (1=TRUE, 0=FALSE)
Use BFGS Minimizer (1=TRUE, 0=FALSE)
Use grouped time points (1=TRUE, 0=FALSE).
Exchange Algorithm Criteria:
1/"modified" = Modified
2/"fedorov" = Fedorov
Filename and path, or function name, for a run file that is used instead of the regular PopED call.
******START OF Labeling and file names SPECIFICATION OPTIONS**********
The current PopED version
The model title
Filename and path of the output file during search
Filename suffix of the result function file
Filename and path for storage of current optimal design
******START OF Miscellaneous SPECIFICATION OPTIONS**********
User defined data structure that, for example could be used to send in data to the model
Value to interpret as zero in design
The seed number used for optimization and sampling -- integer or -1 which creates a random seed as.integer(Sys.time()) or NULL.
Vector for line search on continuous design variables (1=TRUE,0=FALSE)
Vector for line search on discrete design variables (1=TRUE,0=FALSE)
Use graph output during search
If a log file should be used (0=FALSE, 1=TRUE)
Method used to calculate M1:
1/"central" = Central difference
0/"complex" = Complex difference
20/"analytic" = Analytic derivative
30/"ad" = Automatic differentiation
Method used to calculate M2:
1/"central" = Central difference
0/"complex" = Complex difference
20/"analytic" = Analytic derivative
30/"ad" = Automatic differentiation
Method used to calculate linearization of residual error:
1/"central" = Central difference
0/"complex" = Complex difference
30/"ad" = Automatic differentiation
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
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
Method used to calculate all the gradients:
1/"central" = Central difference
0/"complex" = Complex difference
Number of iterations in random search between screen output
Number of iterations in stochastic gradient search between screen output
Step length of derivative of linearized model w.r.t. typical values
Step length of derivative of model w.r.t. g
Step length of derivative of g w.r.t. b
Step length of derivative of variance w.r.t. typical values
Step length of derivative of OFV w.r.t. time
Step length of derivative of model w.r.t. sigma
The absolute tolerance for the diff equation solver
The relative tolerance for the diff equation solver
The diff equation solver method, NULL as default.
If the differential equation results should be stored in memory (1) or not (0)
Number of Random search iterations
Number of stochastic gradient iterations
Number of Random search iterations with discrete optimization.
Number of Stochastic Gradient search iterations with discrete optimization
Iterations until adaptive narrowing in random search
Stochastic Gradient convergence value, (difference in OFV for D-optimal, difference in gradient for ED-optimal)
Random search locality factor for sample times
Random search locality factor for covariates
Stochastic Gradient search first step factor for sample times
Stochastic Gradient search first step factor for covariates
Use greedy algorithm for group assignment optimization
Exchange Algorithm StepSize
Exchange Algorithm NumPoints
Exchange Algorithm Convergence Limit/Criteria
Avoid replicate samples when using Exchange Algorithm
BFGS Minimizer Convergence Criteria Normalized Projected Gradient Tolerance
BFGS Minimizer Line Search Tolerance f
BFGS Minimizer Line Search Tolerance g
BFGS Minimizer Line Search Tolerance x
Number of iterations in ED-optimal design to calculate convergence criteria
ED-optimal design convergence criteria in percent
Number of grid points in the line search
Number of iterations of full Random search and full Stochastic Gradient if line search is not used
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).
logical; only compile the model, do not run
PopED (in conjunction with iCompileOption)
Parallel method to use
0/"matlab"= Matlab PCT
1/"mpi" = MPI
Additional dependencies used in MCC compilation (mat-files), if several space separated
Compilation output executable name
Number of processes to use when running in parallel (e.g. 3 = 2 workers, 1 job manager)
Number of design evaluations that should be evaluated in each process before getting new work from job manager
The prefix of the output mat file to communicate with the executable
Extra options send to e$g. the MPI executable or a batch script, see execute_parallel$m for more information and options
Polling time to check if the parallel execution is finished
The file containing the popedInput structure that should be used to evaluate the designs
If the random search is going to be executed in parallel
If the stochastic gradient search is going to be executed in parallel
If the modified exchange algorithm is going to be executed in parallel
If the line search is going to be executed in parallel
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.
string that represents the time in the dataset (ie xt)
string that represents the lower design time (ie minxt)
string that represents the upper design time (ie maxmt)
The id variable
Number of groups in the study. Each individual in a group will have the same design.
A matrix defining the initial discrete values for the model Each row is a group/individual.
Vector defining the number of samples for each group.
******START OF DESIGN SPACE OPTIONS**********
Max number of samples per group/individual
Min number of samples per group/individual
Number defining the maximum number of samples allowed in the experiment.
Number defining the minimum number of samples allowed in the experiment.
Vector defining the max size of the different groups (max number of individuals in each group)
Vector defining the min size of the different groups (min num individuals in each group) --
The total maximal groupsize over all groups
The total minimal groupsize over all groups
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).
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.
Vector defining the max value for each covariate. If a single value is supplied then all a values are given the same max value
Vector defining the min value for each covariate. If a single value is supplied then all a values are given the same max value
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).
Cell array cell defining the discrete variables for each x value.
Group sampling times between groups so that each group has the same values (TRUE or FALSE).
Matrix defining the grouping of sample points. Matching integers mean that the points are matched.
Allows for finer control than use_grouped_xt
Group continuous design variables between groups so that each group has the same values (TRUE or FALSE).
Matrix defining the grouping of continuous design variables. Matching integers mean that the values are matched.
Allows for finer control than use_grouped_a.
Group discrete design variables between groups so that each group has the same values (TRUE or FALSE).
Matrix defining the grouping of discrete design variables. Matching integers mean that the values are matched.
Allows for finer control than use_grouped_x.
Value to interpret as zero in design.
Filename and path, or function name, for the Autocorrelation function, empty string means no autocorrelation
Filename and path, or function name, for user defined distributions for E-family designs
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
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.
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.
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.
boolean; Fix the residuals to what is specified by the model
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.
[logical(1)]
If TRUE, an existing file in place is allowed if it
it is both readable and writable.
Default is FALSE.
boolean, substitute fixed population values as literals and re-adjust ui and parameter estimates after optimization; Default is `TRUE`.
boolean to indicate if this is meant for optimizing times
boolean to indicate if this is meant for optimizing covariates
boolean to indicate if the discrete design variables be optimized
boolean to indicate if the sample optimizer is
used (not implemented yet in PopED)
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
Matthew L. Fidler