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myfitj
2017 Jun 18
0
R_using non linear regression with constraints
...nd using nlsLM
myfit=nlsLM(y~myfun(a,b,r=2,t=x),data=mydata,start=list(a=2000,b=0.05),
lower = c(1000,0), upper = c(3000,1))
summary(myfit)
library(nlsr)
r <- 2
myfitj=nlxb(y~a*b*(1-exp(-b*r*x)),data=mydata,start=list(a=2000,b=0.05), trace=TRUE)
summary(myfitj)
print(myfitj)
myfitj2<-nlxb(y~ab*(1-exp(-b*r*x)),data=mydata,start=list(ab=2000*0.05,b=0.05), trace=TRUE)
summary(myfitj2)
print(myfitj2)
myfitj2b<-nlxb(y~ab*(1-exp(-b*r*x)),data=mydata,start=list(ab=2000*0.05,b=0.05),
trace=TRUE, upper=c(1000, Inf))
summary(myfitj2b)
print(myfitj2b)
# End of scr...
2017 Jun 18
0
R_using non linear regression with constraints
...,b=0.05),
>> lower = c(1000,0), upper = c(3000,1))
>> summary(myfit)
>> library(nlsr)
>> r <- 2
>> myfitj=nlxb(y~a*b*(1-exp(-b*r*x)),data=mydata,start=list(a=2000,b=0.05), trace=TRUE)
>> summary(myfitj)
>> print(myfitj)
>>
>> myfitj2<-nlxb(y~ab*(1-exp(-b*r*x)),data=mydata,start=list(ab=2000*0.05,b=0.05), trace=TRUE)
>> summary(myfitj2)
>> print(myfitj2)
>>
>> myfitj2b<-nlxb(y~ab*(1-exp(-b*r*x)),data=mydata,start=list(ab=2000*0.05,b=0.05),
>> trace=TRUE, upper=c(1000, Inf))
>...
2017 Jun 18
3
R_using non linear regression with constraints
...,data=mydata,start=list(a=2000,b=0.05),
> lower = c(1000,0), upper = c(3000,1))
> summary(myfit)
> library(nlsr)
> r <- 2
> myfitj=nlxb(y~a*b*(1-exp(-b*r*x)),data=mydata,start=list(a=2000,b=0.05),
> trace=TRUE)
> summary(myfitj)
> print(myfitj)
>
> myfitj2<-nlxb(y~ab*(1-exp(-b*r*x)),data=mydata,start=list(ab=2000*0.05,b=0.05),
> trace=TRUE)
> summary(myfitj2)
> print(myfitj2)
>
> myfitj2b<-nlxb(y~ab*(1-exp(-b*r*x)),data=mydata,start=list(ab=2000*0.05,b=0.05),
> trace=TRUE, upper=c(1000, Inf))
> summary(myfit...
2017 Jun 18
3
R_using non linear regression with constraints
https://cran.r-project.org/web/views/Optimization.html
(Cran's optimization task view -- as always, you should search before posting)
In general, nonlinear optimization with nonlinear constraints is hard,
and the strategy used here (multiplying by a*b < 1000) may not work --
it introduces a discontinuity into the objective function, so
gradient based methods may in particular be