Just write out the 20 terms. On Mon, Sep 21, 2015 at 10:26 PM, Jianling Fan <fanjianl...@gmail.com> wrote:
> Hello, Gabor, > > Thanks again for your suggestion. And now I am trying to improve the > code by adding a function to replace the express "Rm1 * ref.1 + Rm2 * > ref.2 + Rm3 * ref.3 + Rm4 * ref.4 + Rm5 * ref.5 + Rm6 * ref.6" because > I have some other dataset need to fitted to the same model but with > more groups (>20). > > I tried to add the function as: > > denfun<-function(i){ > for(i in 1:6){ > Rm<-sum(Rm[i]*ref.i) > return(Rm)} > } > > but I got another error when I incorporate this function into my > regression: > > >fitdp1<-nlxb(den ~ denfun(6)/(1+(depth/d50)^c), > data = dproot2, > start = c(Rm1=1.01, Rm2=1.01, Rm3=1.01, Rm4=6.65, > Rm5=1.01, Rm6=1, d50=20, c=-1), > masked = "Rm6") > > Error in deriv.default(parse(text = resexp), names(start)) : > Function 'denfun' is not in the derivatives table > > I think there must be something wrong with my function. I tried some > times but am not sure how to improve it because I am quite new to R. > > Could anyone please give me some suggestion. > > Thanks a lot! > > > Jianling > > > On 22 September 2015 at 00:43, Gabor Grothendieck > <ggrothendi...@gmail.com> wrote: > > Express the formula in terms of simple operations like this: > > > > # add 0/1 columns ref.1, ref.2, ..., ref.6 > > dproot2 <- do.call(data.frame, transform(dproot, ref = outer(dproot$ref, > > seq(6), "==") + 0)) > > > > # now express the formula in terms of the new columns > > library(nlmrt) > > fitdp1<-nlxb(den ~ (Rm1 * ref.1 + Rm2 * ref.2 + Rm3 * ref.3 + Rm4 * > ref.4 + > > Rm5 * ref.5 + Rm6 * ref.6)/(1+(depth/d50)^c), > > data = dproot2, > > start = c(Rm1=1.01, Rm2=1.01, Rm3=1.01, Rm4=6.65, Rm5=1.01, > Rm6=1, > > d50=20, c=-1), > > masked = "Rm6") > > > > where we used this input: > > > > Lines <- " depth den ref > > 1 20 0.5730000 1 > > 2 40 0.7800000 1 > > 3 60 0.9470000 1 > > 4 80 0.9900000 1 > > 5 100 1.0000000 1 > > 6 10 0.6000000 2 > > 7 20 0.8200000 2 > > 8 30 0.9300000 2 > > 9 40 1.0000000 2 > > 10 20 0.4800000 3 > > 11 40 0.7340000 3 > > 12 60 0.9610000 3 > > 13 80 0.9980000 3 > > 14 100 1.0000000 3 > > 15 20 3.2083491 4 > > 16 40 4.9683383 4 > > 17 60 6.2381133 4 > > 18 80 6.5322348 4 > > 19 100 6.5780660 4 > > 20 120 6.6032064 4 > > 21 20 0.6140000 5 > > 22 40 0.8270000 5 > > 23 60 0.9500000 5 > > 24 80 0.9950000 5 > > 25 100 1.0000000 5 > > 26 20 0.4345774 6 > > 27 40 0.6654726 6 > > 28 60 0.8480684 6 > > 29 80 0.9268951 6 > > 30 100 0.9723207 6 > > 31 120 0.9939966 6 > > 32 140 0.9992400 6" > > > > dproot <- read.table(text = Lines, header = TRUE) > > > > > > > > On Mon, Sep 21, 2015 at 12:22 PM, Jianling Fan <fanjianl...@gmail.com> > > wrote: > >> > >> Thanks Prof. Nash, > >> > >> Sorry for late reply. I am learning and trying to use your nlmrt > >> package since I got your email. It works good to mask a parameter in > >> regression but seems does work for my equation. I think the problem is > >> that the parameter I want to mask is a group-specific parameter and I > >> have a "[]" syntax in my equation. However, I don't have your 2014 > >> book on hand and couldn't find it in our library. So I am wondering if > >> nlxb works for group data? > >> Thanks a lot! > >> > >> following is my code and I got a error form it. > >> > >> > fitdp1<-nlxb(den~Rm[ref]/(1+(depth/d50)^c),data=dproot, > >> + start =c(Rm1=1.01, Rm2=1.01, Rm3=1.01, Rm4=6.65, > >> Rm5=1.01, Rm6=1, d50=20, c=-1), > >> + masked=c("Rm6")) > >> > >> Error in deriv.default(parse(text = resexp), names(start)) : > >> Function '`[`' is not in the derivatives table > >> > >> > >> Best regards, > >> > >> Jianling > >> > >> > >> On 20 September 2015 at 12:56, ProfJCNash <profjcn...@gmail.com> wrote: > >> > I posted a suggestion to use nlmrt package (function nlxb to be > >> > precise), > >> > which has masked (fixed) parameters. Examples in my 2014 book on > >> > Nonlinear > >> > parameter optimization with R tools. However, I'm travelling just now, > >> > or > >> > would consider giving this a try. > >> > > >> > JN > >> > > >> > > >> > On 15-09-20 01:19 PM, Jianling Fan wrote: > >> >> > >> >> no, I am doing a regression with 6 group data with 2 shared > parameters > >> >> and 1 different parameter for each group data. the parameter I want > to > >> >> coerce is for one group. I don't know how to do it. Any suggestion? > >> >> > >> >> Thanks! > >> >> > >> >> On 19 September 2015 at 13:33, Jeff Newmiller > >> >> <jdnew...@dcn.davis.ca.us> > >> >> wrote: > >> >>> > >> >>> Why not rewrite the function so that value is not a parameter? > >> >>> > >> >>> > >> >>> > --------------------------------------------------------------------------- > >> >>> Jeff Newmiller The ..... ..... Go > >> >>> Live... > >> >>> DCN:<jdnew...@dcn.davis.ca.us> Basics: ##.#. ##.#. > Live > >> >>> Go... > >> >>> Live: OO#.. Dead: OO#.. > >> >>> Playing > >> >>> Research Engineer (Solar/Batteries O.O#. #.O#. > with > >> >>> /Software/Embedded Controllers) .OO#. .OO#. > >> >>> rocks...1k > >> >>> > >> >>> > >> >>> > --------------------------------------------------------------------------- > >> >>> Sent from my phone. Please excuse my brevity. > >> >>> > >> >>> On September 18, 2015 9:54:54 PM PDT, Jianling Fan > >> >>> <fanjianl...@gmail.com> wrote: > >> >>>> > >> >>>> Hello, everyone, > >> >>>> > >> >>>> I am using a nls regression with 6 groups data. I am trying to > coerce > >> >>>> a parameter to 1 by using a upper and lower statement. but I always > >> >>>> get an error like below: > >> >>>> > >> >>>> Error in ifelse(internalPars < upper, 1, -1) : > >> >>>> (list) object cannot be coerced to type 'double' > >> >>>> > >> >>>> does anyone know how to fix it? > >> >>>> > >> >>>> thanks in advance! > >> >>>> > >> >>>> My code is below: > >> >>>> > >> >>>> > >> >>>> > >> >>>>> dproot > >> >>>> > >> >>>> depth den ref > >> >>>> 1 20 0.5730000 1 > >> >>>> 2 40 0.7800000 1 > >> >>>> 3 60 0.9470000 1 > >> >>>> 4 80 0.9900000 1 > >> >>>> 5 100 1.0000000 1 > >> >>>> 6 10 0.6000000 2 > >> >>>> 7 20 0.8200000 2 > >> >>>> 8 30 0.9300000 2 > >> >>>> 9 40 1.0000000 2 > >> >>>> 10 20 0.4800000 3 > >> >>>> 11 40 0.7340000 3 > >> >>>> 12 60 0.9610000 3 > >> >>>> 13 80 0.9980000 3 > >> >>>> 14 100 1.0000000 3 > >> >>>> 15 20 3.2083491 4 > >> >>>> 16 40 4.9683383 4 > >> >>>> 17 60 6.2381133 4 > >> >>>> 18 80 6.5322348 4 > >> >>>> 19 100 6.5780660 4 > >> >>>> 20 120 6.6032064 4 > >> >>>> 21 20 0.6140000 5 > >> >>>> 22 40 0.8270000 5 > >> >>>> 23 60 0.9500000 5 > >> >>>> 24 80 0.9950000 5 > >> >>>> 25 100 1.0000000 5 > >> >>>> 26 20 0.4345774 6 > >> >>>> 27 40 0.6654726 6 > >> >>>> 28 60 0.8480684 6 > >> >>>> 29 80 0.9268951 6 > >> >>>> 30 100 0.9723207 6 > >> >>>> 31 120 0.9939966 6 > >> >>>> 32 140 0.9992400 6 > >> >>>> > >> >>>>> fitdp<-nls(den~Rm[ref]/(1+(depth/d50)^c),data=dproot, > >> >>>> > >> >>>> + start = list(Rm=c(1.01, 1.01, 1.01, 6.65,1.01,1), d50=20, c=-1)) > >> >>>>> > >> >>>>> summary(fitdp) > >> >>>> > >> >>>> > >> >>>> Formula: den ~ Rm[ref]/(1 + (depth/d50)^c) > >> >>>> > >> >>>> Parameters: > >> >>>> Estimate Std. Error t value Pr(>|t|) > >> >>>> Rm1 1.12560 0.07156 15.73 3.84e-14 *** > >> >>>> Rm2 1.57643 0.11722 13.45 1.14e-12 *** > >> >>>> Rm3 1.10697 0.07130 15.53 5.11e-14 *** > >> >>>> Rm4 7.23925 0.20788 34.83 < 2e-16 *** > >> >>>> Rm5 1.14516 0.07184 15.94 2.87e-14 *** > >> >>>> Rm6 1.03658 0.05664 18.30 1.33e-15 *** > >> >>>> d50 22.69426 1.03855 21.85 < 2e-16 *** > >> >>>> c -1.59796 0.15589 -10.25 3.02e-10 *** > >> >>>> --- > >> >>>> Signif. codes: 0 ?**?0.001 ?*?0.01 ??0.05 ??0.1 ??1 > >> >>>> > >> >>>> Residual standard error: 0.1094 on 24 degrees of freedom > >> >>>> > >> >>>> Number of iterations to convergence: 8 > >> >>>> Achieved convergence tolerance: 9.374e-06 > >> >>>> > >> >>>>> fitdp1<-nls(den~Rm[ref]/(1+(depth/d50)^c),data=dproot, > >> >>>> > >> >>>> algorithm="port", > >> >>>> + start = list(Rm=c(1.01, 1.01, 1.01, 6.65, 1.01, 1), d50=20, > c=-1), > >> >>>> + lower = list(Rm=c(1.01, 1.01, 1.01, 6.65, 1.01, 1), d50=20, > c=-1), > >> >>>> + upper = list(Rm=c(2.1, 2.2, 2.12, 12.5, 2.3, 1), d50=50, c=1)) > >> >>>> > >> >>>> Error in ifelse(internalPars < upper, 1, -1) : > >> >>>> (list) object cannot be coerced to type 'double' > >> >>>> > >> >>>> ______________________________________________ > >> >>>> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > >> >>>> https://stat.ethz.ch/mailman/listinfo/r-help > >> >>>> PLEASE do read the posting guide > >> >>>> http://www.R-project.org/posting-guide.html > >> >>>> and provide commented, minimal, self-contained, reproducible code. > >> >>> > >> >>> > >> >> > >> >> > >> >> > >> > > >> > ______________________________________________ > >> > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > >> > https://stat.ethz.ch/mailman/listinfo/r-help > >> > PLEASE do read the posting guide > >> > http://www.R-project.org/posting-guide.html > >> > and provide commented, minimal, self-contained, reproducible code. > >> > >> > >> > >> -- > >> Jianling Fan > >> 樊建凌 > >> > >> ______________________________________________ > >> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > >> https://stat.ethz.ch/mailman/listinfo/r-help > >> PLEASE do read the posting guide > >> http://www.R-project.org/posting-guide.html > >> and provide commented, minimal, self-contained, reproducible code. > > > > > > > > > > -- > > Statistics & Software Consulting > > GKX Group, GKX Associates Inc. > > tel: 1-877-GKX-GROUP > > email: ggrothendieck at gmail.com > > > > -- > Jianling Fan > 樊建凌 > -- Statistics & Software Consulting GKX Group, GKX Associates Inc. tel: 1-877-GKX-GROUP email: ggrothendieck at gmail.com [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.