search for: nlslm

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,...

...pus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Sun, Jun 18, 2017 at 9:43 AM, David Winsemius <dwinsemius at comcast.net> wrote: > >> On Jun 18, 2017, at 6:24 AM, Manoranjan Muthusamy <ranjanmano167 at gmail.com> wrote: >> >> 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)) >>...

> On Jun 18, 2017, at 6:24 AM, Manoranjan Muthusamy <ranjanmano167 at gmail.com> wrote: > > 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)} > > an...

...tes: 1) nlsr has been flagged with a check error by CRAN (though it is in the vignette, and also mentions pandoc a couple of times). I'm working to purge the "bug", and found one on our part, but not necessarily all the issues. 2) I used nlxb that requires an expression for the model. nlsLM can use a function because it is using derivative approximations, while nlxb actually gets a symbolic or automatic derivative if it can, else squawks. JN # Here's the script # # # Manoranjan Muthusamy <ranjanmano167 at gmail.com> # library(minpack.lm) mydata=data.frame(x=c(0,5,9,13,17...

...aggregate( ( objdta$myfun - objdta$y )^2 , objdta[ , c( "a", "b" ) ] , FUN = function( x ) sum( x, na.rm=TRUE ) ) objdtassmin <- objdtass[ which.min( objdtass$x ), ] 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 ) ) a <- as.vector( coef( myfit )[ &...

...$y )^2 > , objdta[ , c( "a", "b" ) ] > , FUN = function( x ) > sum( x, na.rm=TRUE ) > ) > objdtassmin <- objdtass[ which.min( objdtass$x ), ] > > 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 ) > )...

Dear all, I am using the nlsLM function to fit a Lorentzian function to my experimental data. The LM algorithm should allow to specify limits, but the upper limit appears not to work as expected in my code. The parameter 'w', which is peak width at half maximuim always hits the upper limit if the limit is specified. I wo...

...2473,1.2151,1.0591,1.0108,0.914,0.7903,0.7312,0.6613,0.5914,0.5323,0.4462,0.3226,0.2688,0.2366) test <- data.frame(X = X, Y = Y) and I tried fit0 <- nls(Y ~ a * (1 + b * log(1 - c * X^d)), data = test, start = list(a = 1.75, b = .22, c = .99, d = .005)) and library(minpack.lm) fit2 <- nlsLM(Y ~ a * (1 + b * log(1 - c * X^d)), data = test, start = list(a = 1.75, b = .22, c = .99, d = .005), control = nls.lm.control(maxiter = 100)) I'm not an expert in fitting models so I'm wondering if there is something I'm missing. I would appreciate your comments/hints. Thanks PM

...ils with >> the message "Singular gradient". I wish that R could have a more >> reliable built-in nonlinear least squares solver. (I could also be >> holding it wrong.) Meanwhile, we have excellent CRAN packages nlsr and >> minpack.lm: >> >> minpack.lm::nlsLM( >> ? pHobs ~ pHm(SID, pK1, pK2, pK3), >> ? data.frame(pHobs = pHobs, SID = SID), >> ? start = c(pK1 = pK1, pK2 = pK2, pK3 = pK3), >> ? # the following is also needed to avoid MINPACK failing to fit >> ? lower = rep(-1, 3), upper = rep(9, 3) >> ) >> # Nonli...

...obian of the model function cannot be inverted and fails with the message "Singular gradient". I wish that R could have a more reliable built-in nonlinear least squares solver. (I could also be holding it wrong.) Meanwhile, we have excellent CRAN packages nlsr and minpack.lm: minpack.lm::nlsLM( pHobs ~ pHm(SID, pK1, pK2, pK3), data.frame(pHobs = pHobs, SID = SID), start = c(pK1 = pK1, pK2 = pK2, pK3 = pK3), # the following is also needed to avoid MINPACK failing to fit lower = rep(-1, 3), upper = rep(9, 3) ) # Nonlinear regression model # model: pHobs ~ pHm(SID, pK1, pK2, pK3) #...

...annot be inverted and fails with > the message "Singular gradient". I wish that R could have a more > reliable built-in nonlinear least squares solver. (I could also be > holding it wrong.) Meanwhile, we have excellent CRAN packages nlsr and > minpack.lm: > > minpack.lm::nlsLM( > pHobs ~ pHm(SID, pK1, pK2, pK3), > data.frame(pHobs = pHobs, SID = SID), > start = c(pK1 = pK1, pK2 = pK2, pK3 = pK3), > # the following is also needed to avoid MINPACK failing to fit > lower = rep(-1, 3), upper = rep(9, 3) > ) > # Nonlinear regression model > #...

Hi, am very new to R and I've written an optim function, but can't get it to work least.squares.fitter<-function(start.params,gr,low.constraints,high.constraints,model.one.stepper,data,scale,ploton=F) { result<-optim(par=start.params,method=c('Nelder-Mead'),fn=least.squares.fit,lower=low.constraints,upper=high.constraints,data=data,scale=scale,ploton=ploton)

SAS has DUD (Does not Use Derivatives)/Secant Method for nonlinear regression, does R offer this option for nonlinear regression? I have read the helpfile for nls() and could not find such option, any suggestion? Thanks, Derek [[alternative HTML version deleted]]

Dear friends - I have a function for the charge in a fluid (water) buffered with HEPES and otherwise only containing Na and Cl so that [Na] - [Cl] = SID (strong ion difference) goes from -1 mM to 1 mM. With known SID and total HEPES concentration I can calculate accurately the pH if I know 3 pK values for HEPES by finding the single root with uniroot Now, the problem is that there is some