[R] How to coerce a parameter in nls?

[ProfJCNash]

Besides this, using bounds to fix (also called "mask") parameters is 
generally a very bad idea. Some optimization methods allow this 
explicitly. For nonlinear least squares nlmrt package has it, but I'm 
not sure I fully documented the process. For optimization, Rvmmin and 
Rcgmin both allow masks, but again the documentation is not fully 
developed. There is a section in my 2014 book Nonlinear Parameter 
Optimization in R. I'm on vacation, and don't have the page refs. at 
this moment, however.

I know there are some other packages that include the possibility of 
fixed parameters, and perhaps others could give pointers, as I don't 
have the references with me.

JN

On 15-09-19 05:10 AM, Sarah Goslee wrote:
> You need to read the help more closely. start should be a list, as you've
> done, but upper and lower should be vectors instead. Which is exactly what
> your error message is telling you.
>
> lower, upper
>
> vectors of lower and upper bounds, replicated to be as long as start. If
> unspecified, all parameters are assumed to be unconstrained. Bounds can
> only be used with the "port" algorithm. They are ignored, with a warning,
> if given for other algorithms.
> Sarah
>
> On Saturday, September 19, 2015, 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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>
>