# [R] Non-linear system of equations

Paul Smith phhs80 at gmail.com
Fri Apr 25 14:58:08 CEST 2008

```Try to change the initial values of the parameters with, for instance,

p0 <- rnorm(2)

But you sure that your system has a solution, Evgeniq?

Paul

2008/4/25 Radka Pancheva <radica at abv.bg>:
> Hello Paul,
>
>  Thank you for your quick answer. I have tried to use your advice and to estimate the parameters of beta distribution with moments matching. This is my code:
>
>
>  ex <- 0.3914877
>  ex2 <- 0.2671597
>
>  my.mm <- function(x){
>         p <- x[1]
>         q <- x[2]
>         p <-  .Machine\$double.eps
>         q <-  .Machine\$double.eps
>
>         F <- rep(NA,2)
>
>         F[1] <- p/(p + q)
>         F[2]<- (p*q + (p + q + 1)*p^2)/((p + q + 1)*(p + q)^2)
>
>         return(F)
>  }
>
>  p0 <- c(ex,ex2)
>
>  dfsane(par=p0, fn=my.mm,control=list(maxit=50000))
>
>  and I became the following output:
>
>  …
>  iteration:  3640  ||F(xn)|| =   0.7071068
>  iteration:  3641  ||F(xn)|| =   0. 7071068
>  …
>  iteration:  49990  ||F(xn)|| =   0. 7071068
>  iteration:  50000  ||F(xn)|| =   0. 7071068
>  \$par
>  [1] -446.2791 -446.4034
>
>  \$residual
>  [1] 0.5
>
>  \$fn.reduction
>  [1] 0
>
>  \$feval
>  [1] 828495
>
>  \$iter
>  [1] 50001
>
>  \$convergence
>  [1] 1
>
>  \$message
>  [1] "Maximum limit for iterations exceeded"
>
>  I have tried maxiter=100000 but the output is the same. I know that ex and ex2 are bringing the problems, but I am stuck with them. How can I make it convergent?
>
>  Thank you,
>
>  Evgeniq
>
>
>
>   >2008/4/25 Radka Pancheva <radica at abv.bg>:
>   >>  I am trying to estimate the parameters of a bimodal normal distribution using moments matching, so I have to solve a non-linear system of equations. How can I solve the following simple example?
>   >>
>   >>  x^2 - y^2 = 6
>   >>  x – y = 3
>   >>
>   >>  I heard about nlsystemfit, but I don't know how to run it exactly. I have tried the following code, but it doesn't really work:
>   >>
>   >>
>   >>  f1 <-y~ x[1]^2-x[2]^2-6
>   >>  f2 <-z~ x[1]-x[2]-3
>   >>  f  <- list(f1=0,f2=0)
>   >>  nlsystemfit("OLS",f,startvals=c(0,0))
>   >
>   >You could try the recent package BB by Ravi Varadhan. The code could
>   >be the following:
>   >
>   >library(BB)
>   >
>   >f <- function(x) {
>   >  x1 <- x[1]
>   >  x2 <- x[2]
>   >
>   >  F <- rep(NA, 2)
>   >
>   >  F[1] <- x1^2 - x2^2 - 6
>   >  F[2] <- x1 - x2 - 3
>   >
>   >  return(F)
>   >}
>   >
>   >p0 <- c(1,2)
>   >dfsane(par=p0, fn=f,control=list(maxit=3000))
>   >
>   >I got the solution:
>   >
>   >x1 = 2.5
>   >x2 = -0.5
>   >
>   >Paul
>   >
>
>
>  >______________________________________________
>   >R-help at r-project.org mailing list
>   >https://stat.ethz.ch/mailman/listinfo/r-help
>   >PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>   >and provide commented, minimal, self-contained, reproducible code.
>   >
>
>  ______________________________________________
>  R-help at r-project.org mailing list
>  https://stat.ethz.ch/mailman/listinfo/r-help
>  PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>  and provide commented, minimal, self-contained, reproducible code.
>

```

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