Hello Prof. Nash,
My regression works good now. But I found another problem when I using
nlxb. In the output, the SE, t-stat, and p-value are not available.
Furthermore, I can't extract AIC from the output. The output looks
like below:
Do you have any suggestion for this?
Thanks a lot!
Regards,
nlmrt class object: x
residual sumsquares = 0.29371 on 33 observations
after 9 Jacobian and 10 function evaluations
name coeff SE tstat pval
gradient JSingval
Rm1 1.1162 NA NA NA
-3.059e-13 2.745
Rm2 1.56072 NA NA NA
1.417e-13 1.76
Rm3 1.09775 NA NA NA
-3.179e-13 1.748
Rm4 7.18377 NA NA NA
-2.941e-12 1.748
Rm5 1.13562 NA NA NA
-3.305e-13 1.076
Rm6 1 M NA NA NA
0 0.603
d50 22.4803 NA NA NA
4.975e-13 0.117
c -1.64075 NA NA NA
4.12e-12 1.908e-17
On 21 September 2015 at 13:38, ProfJCNash <profjcnash at gmail.com>
wrote:> I've not used it for group data, and suspect that the code to generate
> derivatives cannot cope with the bracket syntax. If you can rewrite the
> equation without the brackets, you could get the derivatives and solve that
> way. This will probably mean having a "translation" routine to
glue things
> together.
>
> JN
>
>
> On 15-09-21 12:22 PM, Jianling Fan 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 <profjcnash at
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 <jdnewmil at
dcn.davis.ca.us>
>>>> wrote:
>>>>>
>>>>>
>>>>> Why not rewrite the function so that value is not a
parameter?
>>>>>
>>>>>
>>>>>
---------------------------------------------------------------------------
>>>>> Jeff Newmiller The .....
..... Go
>>>>> Live...
>>>>> DCN:<jdnewmil at 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
>>>>> <fanjianling at 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 at 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 at 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
???
You may have to do without masking and switch back to nls. dproot2 and fo are from prior post. # to mask Rm6 omit it from start and set it explicitly st <- c(Rm1=1.01, Rm2=1.01, Rm3=1.01, Rm4=6.65, Rm5=1.01, d50=20, c=-1) Rm6 <- 1 fm.nls <- nls(fo, dproot2, start = st) AIC(fm.nls) summary(fm.nls) On Tue, Sep 22, 2015 at 12:46 PM, Jianling Fan <fanjianling at gmail.com> wrote:> Hello Prof. Nash, > > My regression works good now. But I found another problem when I using > nlxb. In the output, the SE, t-stat, and p-value are not available. > Furthermore, I can't extract AIC from the output. The output looks > like below: > > Do you have any suggestion for this? > > Thanks a lot! > > Regards, > > nlmrt class object: x > residual sumsquares = 0.29371 on 33 observations > after 9 Jacobian and 10 function evaluations > name coeff SE tstat pval > gradient JSingval > Rm1 1.1162 NA NA NA > -3.059e-13 2.745 > Rm2 1.56072 NA NA NA > 1.417e-13 1.76 > Rm3 1.09775 NA NA NA > -3.179e-13 1.748 > Rm4 7.18377 NA NA NA > -2.941e-12 1.748 > Rm5 1.13562 NA NA NA > -3.305e-13 1.076 > Rm6 1 M NA NA NA > 0 0.603 > d50 22.4803 NA NA NA > 4.975e-13 0.117 > c -1.64075 NA NA NA > 4.12e-12 1.908e-17 > > > > On 21 September 2015 at 13:38, ProfJCNash <profjcnash at gmail.com> wrote: > > I've not used it for group data, and suspect that the code to generate > > derivatives cannot cope with the bracket syntax. If you can rewrite the > > equation without the brackets, you could get the derivatives and solve > that > > way. This will probably mean having a "translation" routine to glue > things > > together. > > > > JN > > > > > > On 15-09-21 12:22 PM, Jianling Fan 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 <profjcnash at 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 < > jdnewmil at dcn.davis.ca.us> > >>>> wrote: > >>>>> > >>>>> > >>>>> Why not rewrite the function so that value is not a parameter? > >>>>> > >>>>> > >>>>> > --------------------------------------------------------------------------- > >>>>> Jeff Newmiller The ..... ..... Go > >>>>> Live... > >>>>> DCN:<jdnewmil at 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 > >>>>> <fanjianling at 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 at 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 at 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 > ??? > > ______________________________________________ > R-help at 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 [[alternative HTML version deleted]]
great, Thanks a lot! On 22 September 2015 at 12:07, Gabor Grothendieck <ggrothendieck at gmail.com> wrote:> You may have to do without masking and switch back to nls. dproot2 and fo > are from prior post. > > # to mask Rm6 omit it from start and set it explicitly > st <- c(Rm1=1.01, Rm2=1.01, Rm3=1.01, Rm4=6.65, Rm5=1.01, d50=20, c=-1) > Rm6 <- 1 > > fm.nls <- nls(fo, dproot2, start = st) > > AIC(fm.nls) > summary(fm.nls) > > > On Tue, Sep 22, 2015 at 12:46 PM, Jianling Fan <fanjianling at gmail.com> > wrote: >> >> Hello Prof. Nash, >> >> My regression works good now. But I found another problem when I using >> nlxb. In the output, the SE, t-stat, and p-value are not available. >> Furthermore, I can't extract AIC from the output. The output looks >> like below: >> >> Do you have any suggestion for this? >> >> Thanks a lot! >> >> Regards, >> >> nlmrt class object: x >> residual sumsquares = 0.29371 on 33 observations >> after 9 Jacobian and 10 function evaluations >> name coeff SE tstat pval >> gradient JSingval >> Rm1 1.1162 NA NA NA >> -3.059e-13 2.745 >> Rm2 1.56072 NA NA NA >> 1.417e-13 1.76 >> Rm3 1.09775 NA NA NA >> -3.179e-13 1.748 >> Rm4 7.18377 NA NA NA >> -2.941e-12 1.748 >> Rm5 1.13562 NA NA NA >> -3.305e-13 1.076 >> Rm6 1 M NA NA NA >> 0 0.603 >> d50 22.4803 NA NA NA >> 4.975e-13 0.117 >> c -1.64075 NA NA NA >> 4.12e-12 1.908e-17 >> >> >> >> On 21 September 2015 at 13:38, ProfJCNash <profjcnash at gmail.com> wrote: >> > I've not used it for group data, and suspect that the code to generate >> > derivatives cannot cope with the bracket syntax. If you can rewrite the >> > equation without the brackets, you could get the derivatives and solve >> > that >> > way. This will probably mean having a "translation" routine to glue >> > things >> > together. >> > >> > JN >> > >> > >> > On 15-09-21 12:22 PM, Jianling Fan 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 <profjcnash at 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 >> >>>> <jdnewmil at dcn.davis.ca.us> >> >>>> wrote: >> >>>>> >> >>>>> >> >>>>> Why not rewrite the function so that value is not a parameter? >> >>>>> >> >>>>> >> >>>>> >> >>>>> --------------------------------------------------------------------------- >> >>>>> Jeff Newmiller The ..... ..... Go >> >>>>> Live... >> >>>>> DCN:<jdnewmil at 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 >> >>>>> <fanjianling at 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 at 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 at 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 >> ??? >> >> ______________________________________________ >> R-help at 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 ???