Last data update: 2014.03.03

 R: Perform parameter estimation using a genetic algorithm...
parEstimationLBodeR Documentation

Perform parameter estimation using a genetic algorithm (package genalg) or ssm (if package essm available).

Description

This function is an alias to the parEstimationLBode variants (parEstimationLBodeGA and parEstimationLBodeSSm)

Usage

	parEstimationLBode(cnolist, model, method="ga", ode_parameters = NULL, indices = NULL,
	paramsGA=NULL, paramsSSm=NULL)

Arguments

cnolist

A list containing the experimental design and data.

model

The logic model to be simulated.

method

Only "ga" or "essm" arguments are accepted.

ode_parameters

A list with the ODEs parameter information. Obtained with createLBodeContPars.

indices

Indices to map data in the model. Obtained with indexFinder function from CellNOptR.

paramsGA

A list of GA parameters. default is the list returned by defaultParametersGA.

paramsSSm

A list of SSm parameters. default is the list returned bydefaultParametersSSm.

Value

LB_n

A numeric value to be used as lower bound for all parameters of type n.

LB_k

A numeric value to be used as lower bound for all parameters of type k.

LB_tau

A numeric value to be used as lower bound for all parameters of type tau.

UB_n

A numeric value to be used as upper bound for all parameters of type n.

UB_k

A numeric value to be used as upper bound for all parameters of type k.

UB_tau

A numeric value to be used as upper bound for all parameters of type tau.

default_n

The default parameter to be used for every parameter of type n.

default_k

The default parameter to be used for every parameter of type k.

default_tau

The default parameter to be used for every parameter of type tau.

LB_in

An array with the the same length as ode_parameters$parValues with lower bounds for each specific parameter.

UB_in

An array with the the same length as ode_parameters$parValues with upper bounds for each specific parameter.

opt_n

Add all parameter n to the index of parameters to be fitted.

opt_k

Add all parameter k to the index of parameters to be fitted.

opt_tau

Add all parameter tau to the index of parameters to be fitted.

random

A logical value that determines that a random solution is for the parameters to be optimized.

res

A list containing the information provided by the solver.

Author(s)

David Henriques, Thomas Cokelaer

See Also

CellNOptR createLBodeContPars rbga

Examples

data("ToyCNOlist",package="CNORode");
data("ToyModel",package="CNORode");
data("ToyIndices",package="CNORode");
	
ode_parameters=createLBodeContPars(model,random=TRUE);
#Visualize initial solution
simulatedData=plotLBodeFitness(cnolistCNORodeExample, model,ode_parameters,indices=indices)
paramsGA = defaultParametersGA()
paramsGA$maxStepSize = 1
paramsGA$popSize = 10
paramsGA$iter = 10
paramsGA$transfer_function = 2
	
ode_parameters=parEstimationLBode(cnolistCNORodeExample,model,ode_parameters=ode_parameters,
	paramsGA=paramsGA)
#Visualize fitted solution
simulatedData=plotLBodeFitness(cnolistCNORodeExample, model,ode_parameters,indices=indices)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(CNORode)
Loading required package: CellNOptR
Loading required package: RBGL
Loading required package: graph
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Loading required package: hash
hash-2.2.6 provided by Decision Patterns

Loading required package: ggplot2
Loading required package: RCurl
Loading required package: bitops
Loading required package: Rgraphviz
Loading required package: grid
Loading required package: XML

Attaching package: 'XML'

The following object is masked from 'package:graph':

    addNode

Loading required package: genalg

Attaching package: 'CNORode'

The following object is masked from 'package:stats':

    simulate

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/CNORode/parEstimationLBode.Rd_%03d_medium.png", width=480, height=480)
> ### Name: parEstimationLBode
> ### Title: Perform parameter estimation using a genetic algorithm (package
> ###   genalg) or ssm (if package essm available).
> ### Aliases: parEstimationLBode
> ### Keywords: genetic algorithm CellNOptR logic model
> 
> ### ** Examples
> 
> data("ToyCNOlist",package="CNORode");
> data("ToyModel",package="CNORode");
> data("ToyIndices",package="CNORode");
> 	
> ode_parameters=createLBodeContPars(model,random=TRUE);
> #Visualize initial solution
> simulatedData=plotLBodeFitness(cnolistCNORodeExample, model,ode_parameters,indices=indices)
> paramsGA = defaultParametersGA()
> paramsGA$maxStepSize = 1
> paramsGA$popSize = 10
> paramsGA$iter = 10
> paramsGA$transfer_function = 2
> 	
> ode_parameters=parEstimationLBode(cnolistCNORodeExample,model,ode_parameters=ode_parameters,
+ 	paramsGA=paramsGA)
[1] "Iteration:1  Best_f:0.0811702259387156  N_Evals:10"
[1] "Iteration:2  Best_f:0.0811702259387156  N_Evals:20"
[1] "Iteration:3  Best_f:0.0764334115022538  N_Evals:30"
[1] "Iteration:4  Best_f:0.0731238598361861  N_Evals:40"
[1] "Iteration:5  Best_f:0.0731238598361861  N_Evals:50"
[1] "Iteration:6  Best_f:0.0687707683900974  N_Evals:60"
[1] "Iteration:7  Best_f:0.0687707683900974  N_Evals:70"
[1] "Iteration:8  Best_f:0.0660482039463098  N_Evals:80"
[1] "Iteration:9  Best_f:0.0646744229891346  N_Evals:90"
[1] "Iteration:10  Best_f:0.0149106433411131  N_Evals:100"
[1] "Iteration:11  Best_f:0.0149106433411131  N_Evals:110"
[1] "Iteration:12  Best_f:0.00907520448355253  N_Evals:120"
[1] "Iteration:13  Best_f:0.00907520448355253  N_Evals:130"
[1] "Iteration:14  Best_f:0.00896544658052399  N_Evals:140"
[1] "Iteration:15  Best_f:0.00896544658052399  N_Evals:150"
[1] "Iteration:16  Best_f:0.00896544658052399  N_Evals:160"
[1] "Iteration:17  Best_f:0.00896544658052399  N_Evals:170"
[1] "Iteration:18  Best_f:0.00896544658052399  N_Evals:180"
[1] "Iteration:19  Best_f:0.00886230123183909  N_Evals:190"
[1] "Iteration:20  Best_f:0.00886230123183909  N_Evals:200"
[1] "Iteration:21  Best_f:0.00886230123183909  N_Evals:210"
[1] "Iteration:22  Best_f:0.00875153032728864  N_Evals:220"
[1] "Iteration:23  Best_f:0.00875153032728864  N_Evals:230"
[1] "Iteration:24  Best_f:0.00875153032728864  N_Evals:240"
[1] "Iteration:25  Best_f:0.00875153032728864  N_Evals:250"
[1] "Iteration:26  Best_f:0.00875153032728864  N_Evals:260"
[1] "Iteration:27  Best_f:0.00845155994067303  N_Evals:270"
[1] "Iteration:28  Best_f:0.00845155994067303  N_Evals:280"
[1] "Iteration:29  Best_f:0.00845155994067303  N_Evals:290"
[1] "Iteration:30  Best_f:0.00845155994067303  N_Evals:300"
[1] "Iteration:31  Best_f:0.00844213371764765  N_Evals:310"
[1] "Iteration:32  Best_f:0.00844213371764765  N_Evals:320"
[1] "Iteration:33  Best_f:0.00844213371764765  N_Evals:330"
[1] "Iteration:34  Best_f:0.00844213371764765  N_Evals:340"
[1] "Iteration:35  Best_f:0.00844213371764765  N_Evals:350"
[1] "Iteration:36  Best_f:0.00844213371764765  N_Evals:360"
[1] "Iteration:37  Best_f:0.00844213371764765  N_Evals:370"
[1] "Iteration:38  Best_f:0.00844213371764765  N_Evals:380"
[1] "Iteration:39  Best_f:0.00836025486209914  N_Evals:390"
[1] "Iteration:40  Best_f:0.00836025486209914  N_Evals:400"
[1] "Iteration:41  Best_f:0.00836025486209914  N_Evals:410"
[1] "Iteration:42  Best_f:0.00836025486209914  N_Evals:420"
[1] "Iteration:43  Best_f:0.00836025486207869  N_Evals:430"
[1] "Iteration:44  Best_f:0.00836025486207869  N_Evals:440"
[1] "Iteration:45  Best_f:0.00836025486207869  N_Evals:450"
[1] "Iteration:46  Best_f:0.00836025486207869  N_Evals:460"
[1] "Iteration:47  Best_f:0.00836025486207869  N_Evals:470"
[1] "Iteration:48  Best_f:0.00836025486207869  N_Evals:480"
[1] "Iteration:49  Best_f:0.00836025486207869  N_Evals:490"
[1] "Iteration:50  Best_f:0.00740887841400001  N_Evals:500"
[1] "Iteration:51  Best_f:0.00740887841400001  N_Evals:510"
[1] "Iteration:52  Best_f:0.00740887841400001  N_Evals:520"
[1] "Iteration:53  Best_f:0.00740887841400001  N_Evals:530"
[1] "Iteration:54  Best_f:0.00728473444895273  N_Evals:540"
[1] "Iteration:55  Best_f:0.00728473444895273  N_Evals:550"
[1] "Iteration:56  Best_f:0.00728473444895273  N_Evals:560"
[1] "Iteration:57  Best_f:0.00728473444895273  N_Evals:570"
[1] "Iteration:58  Best_f:0.00728473444895273  N_Evals:580"
[1] "Iteration:59  Best_f:0.00728473444895273  N_Evals:590"
[1] "Iteration:60  Best_f:0.00728473444895273  N_Evals:600"
[1] "Iteration:61  Best_f:0.00728473444895273  N_Evals:610"
[1] "Iteration:62  Best_f:0.00639206737947561  N_Evals:620"
[1] "Iteration:63  Best_f:0.00639206737947561  N_Evals:630"
[1] "Iteration:64  Best_f:0.00639206737947561  N_Evals:640"
[1] "Iteration:65  Best_f:0.00639206737947561  N_Evals:650"
[1] "Iteration:66  Best_f:0.00639206737947561  N_Evals:660"
[1] "Iteration:67  Best_f:0.00639206737947561  N_Evals:670"
[1] "Iteration:68  Best_f:0.00639206737947561  N_Evals:680"
[1] "Iteration:69  Best_f:0.00639206737947561  N_Evals:690"
[1] "Iteration:70  Best_f:0.00639206737947561  N_Evals:700"
[1] "Iteration:71  Best_f:0.00639206737947561  N_Evals:710"
[1] "Iteration:72  Best_f:0.00639206737947561  N_Evals:720"
[1] "Iteration:73  Best_f:0.00639206737947561  N_Evals:730"
[1] "Iteration:74  Best_f:0.00639206737947561  N_Evals:740"
[1] "Iteration:75  Best_f:0.00639206737947561  N_Evals:750"
[1] "Iteration:76  Best_f:0.00639206737947561  N_Evals:760"
[1] "Iteration:77  Best_f:0.00529836861199513  N_Evals:770"
[1] "Iteration:78  Best_f:0.00529836861199513  N_Evals:780"
[1] "Iteration:79  Best_f:0.00529836861199513  N_Evals:790"
[1] "Iteration:80  Best_f:0.00529836861199513  N_Evals:800"
[1] "Iteration:81  Best_f:0.00529836861199513  N_Evals:810"
[1] "Iteration:82  Best_f:0.00529836861199513  N_Evals:820"
[1] "Iteration:83  Best_f:0.00528697933820685  N_Evals:830"
[1] "Iteration:84  Best_f:0.00528697933820685  N_Evals:840"
[1] "Iteration:85  Best_f:0.00528697933820685  N_Evals:850"
[1] "Iteration:86  Best_f:0.00528697933820685  N_Evals:860"
[1] "Iteration:87  Best_f:0.00528697933820685  N_Evals:870"
[1] "Iteration:88  Best_f:0.00528697933820685  N_Evals:880"
[1] "Iteration:89  Best_f:0.00464324079465738  N_Evals:890"
[1] "Iteration:90  Best_f:0.00464324079465738  N_Evals:900"
[1] "Iteration:91  Best_f:0.00464324079465738  N_Evals:910"
[1] "Iteration:92  Best_f:0.00463987612014276  N_Evals:920"
[1] "Iteration:93  Best_f:0.00463987612014276  N_Evals:930"
[1] "Iteration:94  Best_f:0.00463974049322177  N_Evals:940"
[1] "Iteration:95  Best_f:0.00463974049322177  N_Evals:950"
[1] "Iteration:96  Best_f:0.00462377443389316  N_Evals:960"
[1] "Iteration:97  Best_f:0.00436281009229486  N_Evals:970"
[1] "Iteration:98  Best_f:0.00433551526046829  N_Evals:980"
[1] "Iteration:99  Best_f:0.00432037068979449  N_Evals:990"
[1] "Iteration:100  Best_f:0.00432037068979449  N_Evals:1000"
> #Visualize fitted solution
> simulatedData=plotLBodeFitness(cnolistCNORodeExample, model,ode_parameters,indices=indices)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>


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