[R] Optimization in R similar to MS Excel Solver

[Richard James]

Dear Berend and Thomas,

thank you for suggesting the lsei function. I found that the tlsce {BCE}
function also works very well:

library("BCE")
tlsce(A=bmat,B=target)

The limSolve package has an 'xsample' function for generating uncertainty
values via Monte-Carlo simulation, however it only works when specifying the
standard deviation on the target data (B). In my situation I have standard
deviations for the source areas (A) only. Therefore I need to generate the
uncertainty values manually.

I've created a matrix of 1000 randonly distributed numbers for each of the
source area (A) properties:
 
TSCa<-matrix(rnorm(1000, mean=0.03, sd=0.005),ncol=1)
TSMg<-matrix(rnorm(1000, mean=0.0073, sd=0.002),ncol=1)
CBCa<-matrix(rnorm(1000, mean=0.6, sd=0.1),ncol=1)
CBMg<-matrix(rnorm(1000, mean=0.0102, sd=0.005),ncol=1)
RCa<-matrix(rnorm(1000, mean=0.2, sd=0.05),ncol=1)
RMg<-matrix(rnorm(1000, mean=0.0141, sd=0.005),ncol=1)
DCa<-matrix(rnorm(1000, mean=0.35, sd=0.1),ncol=1)
DMg<-matrix(rnorm(1000, mean=0.012, sd=0.004),ncol=1)

DistAll<-cbind(TSCa,TSMg,CBCa,CBMg,RCa,RMg,DCa,DMg)
colnames(DistAll)<-c("TopSoilCa","TopSoilMg","ChannelBankCa","ChannelBankMg","RoadCa","RoadMg","DrainCa","DrainMg")

I now want to run the lsei model again:

lsei(A=bmat,B=target,E=matrix(rep(1,4),ncol=4),F=1,G=matrix(rep(1,4),ncol=4),H=0,
fulloutput=TRUE))

but with A=bmat replaced by the appropriate random values in DistAll.  I
assume I could then use the function "replicate" to then run the model 1000
times to generate uncertainty values, e.g.

replicate(n=1000,lsei(A=bmat,B=target,E=matrix(rep(1,4),ncol=4),F=1,G=matrix(rep(1,4),ncol=4),H=0,
fulloutput=TRUE))

Would anyone be able to help me write a function to replace bmat with new
values from DistAll each time the lsei model is run?


Many thanks in advance

Richard



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