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.Rbuildignore

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+^.*\.Rproj$
+^\.Rproj\.user$
+^LICENSE.md$

.Rbuildignore~

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+^.*\.Rproj$
+^\.Rproj\.user$

.gitignore

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+.Rproj.user
+.Rhistory
+.RData

DESCRIPTION

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+Package: MBAOD
+Type: Package
+Title: Model Based Adaptive Optimal Design
+Version: 0.0.1
+Date: 2014-03-25
+Author: Andrew Hooker <[email protected]>, Coen Van Hasselt
+Maintainer: Andrew Hooker <[email protected]>
+Description: Adaptive optimal design using nonlinear mixed effect models
+License: LGPL (>= 3)
+Copyright: 2014 Andrew C. Hooker
+

LICENSE.md

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+GNU LESSER GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+
+  This version of the GNU Lesser General Public License incorporates
+the terms and conditions of version 3 of the GNU General Public
+License, supplemented by the additional permissions listed below.
+
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MATLAB/feps.m

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+function [y,globalStructure] = feps(model_switch,xt,g,epsi,globalStructure)
+% -- Auto generated error model file for model One compartment IV bolus dose --
+% -- Created: 04-Apr-2012 19:13:30
+[y,globalStructure]=feval(globalStructure.ff_pointer,model_switch,xt,g,globalStructure);
+%y = y.*(1+epsi(:,1));
+y = y.*(1+epsi(:,1))+epsi(:,2);
+
+end

MATLAB/ff.m

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+function [y,globalStructure]=ff(model_switch,xt,g,globalStructure)
+
+    y=xt;
+
+    %g = {CL,V,Dose}
+
+    tvcl=g(1);
+    tvv=g(2);
+
+    emax=g(3);
+    ec50=g(4);
+    h=g(5);
+
+    wt=g(6);    % covariate a(1)
+
+
+    cl=tvcl+((emax*wt^h)/(ec50^h+wt^h));    
+    v=g(2)*(wt/70);
+    dose=1000*(wt/70);    
+
+    y=((dose/v).*(exp(-cl/v.*xt)));
+
+end 

MATLAB/function_input_reduced.m

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+popedInput.strPopEDVersion='2.12';
+popedInput.nx=0;
+popedInput.ndocc=0;
+popedInput.NumOcc=0; % -- Number of occassions --
+popedInput.iFOCENumInd=1000; % -- Num indivduals in each conditional step --
+popedInput.bUseRandomSearch=1; % -- Use random search (1=true, 0=false) --
+popedInput.bUseStochasticGradient=0; % -- Use Stochastic Gradient search (1=true, 0=false) --
+popedInput.bUseLineSearch=0; % -- Use Line search (1=true, 0=false) --
+popedInput.bUseExchangeAlgorithm=0; % -- Use Exchange algorithm (1=true, 0=false) --
+popedInput.bUseBFGSMinimizer=0; % -- Use BFGS Minimizer (1=true, 0=false) --
+popedInput.line_optx=zeros(1,0)'; % -- Vector for line search on discrete design variables (1=true,0=false) --
+popedInput.dSeed=-1; % -- The seed number used for optimization and sampling --
+popedInput.design.discrete_x=cell(0,1)'; % -- Cell defining the discrete variables --
+popedInput.bUseGrouped_x=0; % -- Use grouped discrete design variables (1=true, 0=false) --
+popedInput.ff_file='ff.m'; % -- Filname and path of the model file --
+popedInput.fg_file='sfg.m'; % -- Filname and path of the g parameter file --
+popedInput.fError_file='feps.m'; % -- Filname and path of the error model file --
+popedInput.strUserDistributionFile='';% -- Filname and path for the user defined distributions --
+popedInput.strEDPenaltyFile='';% -- Filname and path for the ED penalty function, empty string means no penalty --
+popedInput.strAutoCorrelationFile='';% -- Filname and path for the Autocorrelation function, empty string means no autocorrelation --
+popedInput.modtit='One compartment IV bolus dose';% -- The model title --
+popedInput.bShowGraphs=0;% -- Use graph output during search --
+popedInput.use_logfile=0;% -- If a log file should be used (0=false, 1=true) --
+popedInput.output_file='';
+popedInput.output_function_file='';
+popedInput.strIterationFileName='';% -- Filename and path for storage of current optimal design --
+popedInput.m1_switch=0;% -- Method used to calculate M1 (0=Complex difference, 1=Central difference, 20=Analytic derivative, 30=Automatic differentiation) --
+popedInput.m2_switch=0;% -- Method used to calculate M2 (0=Central difference, 1=Central difference, 20=Analytic derivative, 30=Automatic differentiation) --
+popedInput.hle_switch=0;% -- Method used to calculate linearization of residual error (0=Complex difference, 1=Central difference, 30=Automatic differentiation) --
+popedInput.gradff_switch=0;% -- Method used to calculate the gradient of the model (0=Complex difference, 1=Central difference, 20=Analytic derivative, 30=Automatic differentiation) --
+popedInput.gradfg_switch=0;% -- Method used to calculate the gradient of the parameter vector g (0=Complex difference, 1=Central difference, 20=Analytic derivative, 30=Automatic differentiation) --
+popedInput.bLHS=0;% -- Use normal random sampling or Latin Hypercube sampling, 0=Random Sampling, 1=LatinHyperCube --
+popedInput.ourzero=1e-05;% -- Value to interpret as zero in design --
+popedInput.rsit_output=5;% -- Number of iterations in random search between screen output --
+popedInput.sgit_output=1;% -- Number of iterations in stochastic gradient search between screen output --
+popedInput.hm1=0.0001;% -- Step length of derivative of linearized model w.r.t. typical values --
+popedInput.hlf=0.0001;% -- Step length of derivative of model w.r.t. g --
+popedInput.hlg=0.0001;% -- Step length of derivative of g w.r.t. b --
+popedInput.hm2=0.0001;% -- Step length of derivative of variance w.r.t. typical values --
+popedInput.hgd=0.0001;% -- Step length of derivative of OFV w.r.t. time --
+popedInput.hle=0.0001;% -- Step length of derivative of model w.r.t. sigma --
+popedInput.AbsTol=1e-10;% -- The absolute tolerance for the diff equation solver --
+popedInput.RelTol=1e-10;% -- The relative tolerance for the diff equation solver --
+popedInput.iDiffSolverMethod=0;% -- The diff equation solver method, 0=ode45, 1=ode15s --
+popedInput.bUseMemorySolver=0;% -- If the differential equation results should be stored in memory or not --
+popedInput.iFIMCalculationType=0;% -- Fisher Information Matrix type (0=Full FIM, 1=Reduced FIM) --
+popedInput.rsit=25;% -- Number of Random search iterations --
+popedInput.sgit=5;% -- Number of Stochastic gradient search iterations --
+popedInput.intrsit=100;% -- Number of Random search iterations when discrete optimization --
+popedInput.intsgit=50;% -- Number of Stochastic Gradient search iterations when discrete optimization --
+popedInput.maxrsnullit=50;% -- Iterations until adaptive narrowing in random search --
+popedInput.convergence_eps=1e-60;% -- Stoachstic Gradient convergence value, (difference in OFV for D-optimal, difference in gradient for ED-optimal) --
+popedInput.rslxt=4;% -- Random search locality factor for sample times --
+popedInput.rsla=4;% -- Random search locality factor for covariates --
+popedInput.cfaxt=0.001;% -- Stochastic Gradient search first step factor for sample times --
+popedInput.cfaa=0.001;% -- Stochastic Gradient search first step factor for covariates --
+popedInput.bGreedyGroupOpt=0;% -- Use greedy algorithm for group assignment optimization --
+popedInput.EACriteria=1;% -- Exchange Algorithm Criteria, 1 = Modified, 2 = Fedorov --
+popedInput.EAStepSize=0.01;% -- Exchange Algorithm StepSize --
+popedInput.EANumPoints=0;% -- Exchange Algorithm NumPoints --
+popedInput.EAConvergenceCriteria=1e-20;% -- Exchange Algorithm Convergence Limit/Criteria --
+popedInput.bEANoReplicates=0;% -- Avoid replicate samples when using Exchange Algorithm --
+popedInput.BFGSConvergenceCriteriaMinStep=1e-08;% -- BFGS Minimizer Convergence Criteria Minimum Step --
+popedInput.BFGSProjectedGradientTol=0.0001;% -- BFGS Minimizer Convergence Criteria Normalized Projected Gradient Tolerance --
+popedInput.BFGSTolerancef=0.001;% -- BFGS Minimizer Line Search Tolerance f --
+popedInput.BFGSToleranceg=0.9;% -- BFGS Minimizer Line Search Tolerance g --
+popedInput.BFGSTolerancex=0.1;% -- BFGS Minimizer Line Search Tolerance x --
+popedInput.ED_samp_size=45;% -- Sample size for ED-optimal design distribution --
+popedInput.ED_diff_it=30;% -- Number of iterations in ED-optimal design to calculate convergence criteria --
+popedInput.ED_diff_percent=10;% -- ED-optimal design convergence criteria in percent --
+popedInput.line_search_it=50;% -- Number of grid points in the line search --
+popedInput.iNumSearchIterationsIfNotLineSearch=10;% -- Number of iterations of full Random search and full Stochastic Gradient if line search is not used --
+
+popedInput.parallelSettings.iCompileOption=-1;% -- Compile option for PopED (-1 = No compilation, 0 or 3 = Full compilation, 1 or 4 = Only using MCC (shared lib), 2 or 5 = Only MPI, Option 0,1,2 runs PopED and option 3,4,5 ends after compilation --
+popedInput.parallelSettings.iUseParallelMethod=1;% -- Parallel method to use (0 = Matlab PCT, 1 = MPI) --
+popedInput.parallelSettings.strAdditionalMCCCompilerDependencies='';% -- Additional dependencies used in MCC compilation (mat-files), if several; space separated --
+popedInput.parallelSettings.strExecuteName='calc_fim.exe';% -- Compilation output executable name --
+popedInput.parallelSettings.iNumProcesses=2;% -- Number of processes to use when running in parallel (E.g. 3 = 2 workers, 1 job manager) --
+popedInput.parallelSettings.iNumChunkDesignEvals=-1;% -- Number of design evaluations that should be evaluated in each process before getting new work from job manager --
+popedInput.parallelSettings.strMatFileInputPrefix='parallel_input';% -- The prefix of the input mat file to communicate with the excutable --
+popedInput.parallelSettings.strMatFileOutputPrefix='parallel_output';% -- The prefix of the output mat file to communicate with the excutable --
+popedInput.parallelSettings.strExtraRunOptions='';% -- Extra options send to e.g. the MPI exectuable or a batch script, see execute_parallel.m for more information and options --
+popedInput.parallelSettings.dPollResultTime=1.000000e-01;% -- Polling time to check if the parallel execution is finished --
+popedInput.parallelSettings.strFunctionInputName='function_input';% -- The file containing the popedInput structure that should be used to evaluate the designs --
+popedInput.parallelSettings.bParallelRS=0;% -- If the random search is going to be executed in parallel --
+popedInput.parallelSettings.bParallelSG=0;% -- If the stochastic gradient search is going to be executed in parallel --
+popedInput.parallelSettings.bParallelLS=0;% -- If the line search is going to be executed in parallel --
+popedInput.parallelSettings.bParallelMFEA=0;% -- If the modified exchange algorithm is going to be executed in parallel --
+popedInput.user_data={};% -- User defined data structure that e.g. could be used to send in data to the model --
+end

MATLAB/sfg.m

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+function pop_params = sfg(x,a,bpop,b,bocc)
+
+% -- Auto generated parameter definition file --
+% -- Created: 04-Apr-2012 19:13:30
+% -- {CL,V,Wt} --
+
+
+pop_params=[ bpop(1)*exp(b(1))	%g(1)=CL
+ bpop(2)*exp(b(2))	%g(2)=V 
+ bpop(3) % emax
+ bpop(4)  % ec50
+ bpop(5) % hill
+ a(1)];	% Wt
+
+end

MBAOD.Rproj

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+Version: 1.0
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 2
+Encoding: UTF-8
+
+RnwWeave: Sweave
+LaTeX: pdfLaTeX
+
+BuildType: Package
+PackageInstallArgs: --no-multiarch --with-keep.source

NAMESPACE

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+exportPattern("^[[:alpha:]]+")