optimization
There are lots of errors in your code. In particular, the optimization routines do not like functions that ignore the parameters.
I would like to nominate this delicious riposte as a fortune candidate. Anyone to second the motion? Dennis
On Sat, Nov 16, 2013 at 1:26 PM, Prof J C Nash (U30A) <nashjc at uottawa.ca> wrote:
There are lots of errors in your code. In particular, the optimization routines do not like functions that ignore the parameters. And you have not provided out or out1 to the optimizer -- they are returned as elements of func(), but not correctly. Please try some of the examples for optim or optimx to learn how to structure your problem. JN On 13-11-16 06:00 AM, r-help-request at r-project.org wrote:
Message: 19
Date: Fri, 15 Nov 2013 09:17:47 -0800 (PST)
From: IZHAK shabsogh <ishaqbaba at yahoo.com>
To: "r-help at r-project.org" <r-help at r-project.org>
Subject: [R] optimization
Message-ID:
<1384535867.58595.YahooMailNeo at web142506.mail.bf1.yahoo.com>
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x1<-c(5.548,4.896,1.964,3.586,3.824,3.111,3.607,3.557,2.989,18.053,3.773,1.253,2.094,2.726,1.758,5.011,2.455,0.913,0.890,2.468,4.168,4.810,34.319,1.531,1.481,2.239,4.204,3.463,1.727)
y<-c(2.590,3.770,1.270,1.445,3.290,0.930,1.600,1.250,3.450,1.096,1.745,1.060,0.890,2.755,1.515,4.770,2.220,0.590,0.530,1.910,4.010,1.745,1.965,2.555,0.770,0.720,1.730,2.860,0.760)
x2<-c(0.137,2.499,0.419,1.699,0.605,0.677,0.159,1.699,0.340,2.899,0.082,0.425,0.444,0.225,0.241,0.099,0.644,0.266,0.351,0.027,0.030,3.400,1.499,0.351,0.082,0.518,0.471,0.036,0.721)
k<-rep(1,29)
x<-data.frame(k,x1,x2)
freg<-function(y,x1,x2){
reg<- rlm(y ~ x1 + x2 , data=x)
return(reg)
}
func <- function(x1,x2,b){
fit<-freg(y,x1,x2)
b<-c(coef(fit))
dv<-1+ b[2]*x2^b[3]
dv1<-b[2]*x2^b[3]*log(x2)
out <- ( x1/(1+ b[2]*x2^b[3]))
out1<- c(-x1*x2^b[3]/dv^2,-x1* dv1/dv^2)
return(list( out,out1))
}>
optim(par=c(b[2],b[3]), fn=out, gr =out1,
method = c("BFGS"),
lower = -Inf, upper = Inf,
control = list(), hessian = T)
can someone help me try running this code because i try many occasion but prove abortive. the aim is
to optimize the parameter of the model that is parameter estimates using optimization
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