search for: lmdif

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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)) &gt...