fast-package {fast} | R Documentation |
The Fourier Amplitute Sensitivity Test (FAST) is a method to deterimine global sensitivities of a model on parameter changes with relavtively few model runs. This package implements this sensitivity analysis method.
Package: | fast |
Type: | Package |
Version: | 0.5 |
Date: | 2007-12-15 |
License: | GPL2 |
Generate a set of parameter sets with the function fast_parameters
. Run
your model with each parameter set. sensitivity
then evaluates
the sensitivities of the model results on each of the parameters.
Dominik Reusser Maintainer: Dominik Reusser <dreusser@uni-potsdam.de>
CUKIER, R. I.; LEVINE, H. B. & SHULER, K. E. Non-Linear Sensitivity Analysis Of Multi-Parameter Model Systems Journal Of Computational Physics, 1978 , 26 , 1-42
CUKIER, R. I.; FORTUIN, C. M.; SHULER, K. E.; PETSCHEK, A. G. & SCHAIBLY, J. H. Study Of Sensitivity Of Coupled Reaction Systems To Uncertainties In Rate Coefficients .1. Theory Journal Of Chemical Physics, 1973 , 59 , 3873-3878
SCHAIBLY, J. H. & SHULER, K. E. Study Of Sensitivity Of Coupled Reaction Systems To Uncertainties In Rate Coefficients .2. Applications Journal Of Chemical Physics, 1973 , 59 , 3879-3888
CUKIER, R. I.; SCHAIBLY, J. H. & SHULER, K. E. Study Of Sensitivity Of Coupled Reaction Systems To Uncertainties In Rate Coefficients .3. Analysis Of Approximations Journal Of Chemical Physics, 1975 , 63 , 1140-1149
#A simple model depending on two #parameters and an additional #"hyperparameter" x. Depending on #x the model is sensitive to p[1] only (x=1) #or p[2] only (x=0) or both (0<x<1) example_model1<-function(p,x){ return(p[1]*x+p[2]*(1-x)) } paras<-fast_parameters(minimum=c(0,0,0),maximum=c(1,1,1)) paras model_results <- apply(paras, 1, example_model1, x=0.5) plot(model_results) sensitivity <- sensitivity(x=model_results, numberf=3, make.plot=TRUE) sensitivity #In the second example, sensitivities are calculated for #200 model results (which might be a time series). # #The model depends on 4 parameters # #It produces a weighted sum of the 4 parameters and returns this sum # #The weights depend on an additional parameter x=1:200 example_model2(p=c(1,3,1,1),fig=TRUE) example_model2(p=c(1,2,2,3),fig=TRUE) paras<-fast_parameters(min=c(0,0,0,0),max=c(1,2,2,3)) paras model_results <- apply(paras, 1, example_model2) plot(model_results) x11() sensitivity <- sensitivity_rep(data = model_results, xval=1:200, direction = 1, order=4 , numberf=4) p.sensitivity(sen=sensitivity, xval=1:200, legend=names(paras))