[R] How to coerce a parameter in nls?

[ProfJCNash]

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'
>>>
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>>
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