R help - Apr 2012 - Use of optim to fit two curves at the same time ?

Dear list,

Here is a small example code that use optim and optimize in order to fit
two functions.
Is it possible to fit two functions (like those two for example) at the
same time using optim ... or another function in R ?

Thanks

Arnaud

######################################################################
## function 1
x1 <- 1:100
y1 <- 5.468 * x + 3 # + rnorm(100,0, 10)
dfxy <- cbind(x1,y1)

# Objective function
optfunc <- function(x, dfxy){
  a <- x[1]
  b <- x[2]
  xtest <- dfxy[,1]
  yobs <- dfxy[,2]
  ysim <- a*xtest + b
  sum((ysim - yobs)^2)
}

out<- optim(par=c(0.2,5), fn=function(x){optfunc(x, dfxy)}, method
"Nelder-Mead", hessian = F)


## function 2

x2 <- seq(0.01, 0.1, length=100)
y2 <- exp(30*x2)
dfxy2 <- cbind(x2,y2)

# objective function
optfunc2 <- function(x, dfxy){
  a <- x[1]
  xtest <- dfxy[,1]
  yobs <- dfxy[,2]
  ysim <- exp(a*xtest)
  sum((ysim - yobs)^2)
}

out<- optimize(f=function(x){optfunc2(x, dfxy2)}, interval=c(0,500))

######################################################################

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Dear list,

In order to find a solution to my problem, I created a third objective
function including both calculations done in the previous cases. This
function return a value (i.e. the value to be minimize by optim) equal to
the sum of the two sum of squares, but it does not work (see the code added
at the end of my previous script).

Any suggestion ?

Arnaud


Dear list,
>
> Here is a small example code that use optim and optimize in order to fit
> two functions.
> Is it possible to fit two functions (like those two for example) at the
> same time using optim ... or another function in R ?
>
> Thanks
>
> Arnaud
>
> ######################################################################
> ## function 1
> x1 <- 1:100
> y1 <- 5.468 * x + 3 # + rnorm(100,0, 10)
> dfxy <- cbind(x1,y1)
>
> # Objective function
> optfunc <- function(x, dfxy){
>   a <- x[1]
>   b <- x[2]
>   xtest <- dfxy[,1]
>   yobs <- dfxy[,2]
>   ysim <- a*xtest + b
>   sum((ysim - yobs)^2)
> }
>
> out<- optim(par=c(0.2,5), fn=function(x){optfunc(x, dfxy)}, method >
"Nelder-Mead", hessian = F)
>
>
> ## function 2
>
> x2 <- seq(0.01, 0.1, length=100)
> y2 <- exp(30*x2)
> dfxy2 <- cbind(x2,y2)
>
> # objective function
> optfunc2 <- function(x, dfxy){
>   a <- x[1]
>   xtest <- dfxy[,1]
>   yobs <- dfxy[,2]
>   ysim <- exp(a*xtest)
>   sum((ysim - yobs)^2)
> }
>
> out<- optimize(f=function(x){optfunc2(x, dfxy2)}, interval=c(0,500))
>
> ######################################################################
>
>
optfunc3 <- function(x,  dfxy, dfxy2){

  a <- x[1]
  b <- x[2]
  xtest <- dfxy[,1]
  yobs <- dfxy[,2]
  ysim <- a*xtest + b
  obj1 <- sum((ysim - yobs)^2)

  c <- x[3]
  xtest2 <- dfxy2[,1]
  yobs2 <- dfxy2[,2]
  ysim2 <- exp(c*xtest2)
  obj2 <- sum((ysim2 - yobs2)^2)

obj1 + obj2
}

out3<- optim(par=c(0.2,5, 500), fn=function(x){optfunc3(x, dfxy, dfxy2)},
method = "Nelder-Mead", hessian = F)

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