Displaying 8 results from an estimated 8 matches for "lmdif".
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ldif
2017 Jun 18
0
R_using non linear regression with constraints
...gt; coef(myfit)
a b
9.000000e+03 1.227091e-02
> myfit=nlsLM(y~myfun2(a,b,r=2,t=x),data=mydata,start=list(a=2000,b=0.05),
+ lower = c(0,0), upper = c(10^6,1))
Warning message:
In nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, :
lmdif: info = -1. Number of iterations has reached `maxiter' == 50.
#---------
myfit=nlsLM(y~myfun2(a,b,r=2,t=x),data=mydata,start=list(a=2000,b=0.05),
lower = c(0,0), upper = c(10^6,1), control=list(maxiter=100))
prod(coef(myfit))
coef(myfit)
#============
> prod(coef(myfit))...
2017 Jun 18
2
R_using non linear regression with constraints
I am using nlsLM {minpack.lm} to find the values of parameters a and b of
function myfun which give the best fit for the data set, mydata.
mydata=data.frame(x=c(0,5,9,13,17,20),y = c(0,11,20,29,38,45))
myfun=function(a,b,r,t){
prd=a*b*(1-exp(-b*r*t))
return(prd)}
and 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),
2008 Mar 13
0
new version of minpack.lm
The package minpack.lm allows nonlinear regression problems to be
addressed with a modification of the Levenberg-Marquardt algorithm based
on the implementation of 'lmder' and 'lmdif' in MINPACK. Version 1.0-8 of
the package is now available on CRAN.
Changes in version 1.0-8 include:
o possibility to obtain standard error estimates on the parameters
via new methods for the generic functions 'summary' and 'vcov'
o possibility to extract other...
2008 Mar 13
0
new version of minpack.lm
The package minpack.lm allows nonlinear regression problems to be
addressed with a modification of the Levenberg-Marquardt algorithm based
on the implementation of 'lmder' and 'lmdif' in MINPACK. Version 1.0-8 of
the package is now available on CRAN.
Changes in version 1.0-8 include:
o possibility to obtain standard error estimates on the parameters
via new methods for the generic functions 'summary' and 'vcov'
o possibility to extract other...
2017 Jun 18
3
R_using non linear regression with constraints
...b
> 9.000000e+03 1.227091e-02
>
>> myfit=nlsLM(y~myfun2(a,b,r=2,t=x),data=mydata,start=list(a=2000,b=0.05),
> + lower = c(0,0), upper = c(10^6,1))
> Warning message:
> In nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, :
> lmdif: info = -1. Number of iterations has reached `maxiter' == 50.
>
> #---------
> myfit=nlsLM(y~myfun2(a,b,r=2,t=x),data=mydata,start=list(a=2000,b=0.05),
> lower = c(0,0), upper = c(10^6,1), control=list(maxiter=100))
> prod(coef(myfit))
>
> coef(myfit)
> #...
2017 Jun 18
0
R_using non linear regression with constraints
...03 1.227091e-02
>>
>>> myfit=nlsLM(y~myfun2(a,b,r=2,t=x),data=mydata,start=list(a=2000,b=0.05),
>> + lower = c(0,0), upper = c(10^6,1))
>> Warning message:
>> In nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, :
>> lmdif: info = -1. Number of iterations has reached `maxiter' == 50.
>>
>> #---------
>> myfit=nlsLM(y~myfun2(a,b,r=2,t=x),data=mydata,start=list(a=2000,b=0.05),
>> lower = c(0,0), upper = c(10^6,1), control=list(maxiter=100))
>> prod(coef(myfit))
>&g...
2017 Jun 18
3
R_using non linear regression with constraints
...gt;> myfit=nlsLM(y~myfun2(a,b,r=2,t=x),data=mydata,start=list(a=2000,b=0.05),
>>> + lower = c(0,0), upper = c(10^6,1))
>>> Warning message:
>>> In nls.lm(par = start, fn = FCT, jac = jac, control = control, lower =
>>> lower, :
>>> lmdif: info = -1. Number of iterations has reached `maxiter' == 50.
>>>
>>> #---------
>>> myfit=nlsLM(y~myfun2(a,b,r=2,t=x),data=mydata,start=list(a=2000,b=0.05),
>>> lower = c(0,0), upper = c(10^6,1),
>>> control=list(maxiter=100))
>...
2017 Jun 18
0
R_using non linear regression with constraints
...;> myfit=nlsLM(y~myfun2(a,b,r=2,t=x),data=mydata,start=list(a=2000,b=0.05),
>>>> + lower = c(0,0), upper = c(10^6,1))
>>>> Warning message:
>>>> In nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, :
>>>> lmdif: info = -1. Number of iterations has reached `maxiter' == 50.
>>>>
>>>> #---------
>>>> myfit=nlsLM(y~myfun2(a,b,r=2,t=x),data=mydata,start=list(a=2000,b=0.05),
>>>> lower = c(0,0), upper = c(10^6,1), control=list(maxiter=100))
>...