[R] Regression of complex-valued functions

[Rolf Turner]

I have not the mental energy to go through your somewhat complicated 
example, but I suspect that your problem is simply the following:  The 
function nls() is trying to minimize a sum of squares, and that does not 
make sense in the context of complex observations.  That is, nls() is 
trying to minimize a sum of terms of the form

	(f(x_i,theta) - z_i)^2

where f(x_i,theta) and z_i are complex quantities (and theta is the 
vector of parameters with respect to which minimization is "trying to be 
done".  But as I said, this makes no sense.  Not that
(f(x_i,theta) - z_i)^2 is a complex quantity and *not* a positive real 
quantity.

You need to minimize a sum of terms of the form

	|f(x_i,theta) - z_i|^2

where |w|^2 equal to w times the complex conjugate of w.  You get this 
in R via the Mod() function, i.e. |w|^2 is (Mod(w))^2.

Thus it seems to me that nls() cannot handle what you want handled.  You 
need to code up the function to be minimized yourself, using Mod() and 
then use optim() or nlm() to minimize it that function with respect to 
"theta".

HTH

cheers,

Rolf Turner


On 10/02/14 11:45, Andrea Graziani wrote:
> Hi everyone,
>
> I previously posted this question but my message was not well written and did not contain any code so I will try to do a better job this time.
>
> The goal is to perform a non-linear regression on complex-valued data.
> I will first give a short description of the data and then describe the complex-valued non-linear function.
>
> ***The Data
> Obtained from mechanical tests performed at 5 loading frequencies 0.1, 0.25, 1, 0.4 and 12 Hz, and at 5 temeperatures.
> The independent variable used in the regression is:
> f_data <- rep(c(0.1,0.25,1,4,12),5)
>
> The measured values of the response variable are:
> E <- c(289.7411+ 225.0708i , 386.4417+ 303.5021i ,  671.5132+ 521.1253i , 1210.8638+ 846.6253i , 1860.9623+1155.4139i ,
> 862.8984+ 636.2637i , 1159.0436+ 814.5609i , 1919.0369+1186.5679i , 3060.7207+1573.6088i  , 4318.1781+1868.4761i  ,
> 2760.7782+1418.5450i , 3306.3013+1612.2712i , 4746.6958+1923.8662i , 6468.5769+2148.9502i , 8072.2642+2198.5344i  ,
> 6757.7680+2061.3110i , 7591.9787+2123.9168i , 9522.9471+2261.8489i , 11255.0952+2166.6411i , 12601.3970+2120.7178i ,
> 11913.6543+2016.0828i , 12906.8294+2030.0610i , 14343.7693+1893.4877i , 15942.7703+1788.0910i , 16943.2261+1665.9847i)
>
> To visualize the data:
> plot(f_data,Re(E),log="xy")
> plot(f_data,Im(E),log="xy")
> plot(E)
>
> ***Non-linear regression function:
> Obtained from an analytical model
>
> E_2S2P1D <- function(f,logaT,Eg,Ee,k,h,delta,logbeta,logtau)
>             Ee+(Eg-Ee)*(
>             1+delta*(2i*pi*10^(logtau)*f*10^logaT)^-k +
>                     (2i*pi*10^(logtau)*f*10^logaT)^-h +
>                     (2i*pi*10^(logtau)*f*10^logaT*10^(logbeta))^-1
>                       )^-1
>
> E_2S2P1D is a complex-valued function (note the imaginary unit "i") where:
> "f" is the real-valued independent variable (i.e. the frequency),
> "logaT","Eg","Ee","k","h","delta","logbeta","logtau" are real-valued parameters.
>
> For example:
>
>> E_2S2P1D(1,0,27000,200,0.16,0.47,1.97,2.2,0.02)
> [1] 9544.759+2204.974i
>
>> E_2S2P1D(1,-1,27000,200,0.16,0.47,1.97,2.2,0.02)
> [1] 6283.748+2088.473i
>
> In order to find the parameters of the regression function I use “nls".
>
> Executing
>
> fit <- nls(E ~ E_2S2P1D(f_data,c(rep(loga40,5),rep(loga30,5),rep(loga20,5),rep(0,5),rep(loga0,5)),
>                              Eg,Ee,k,h,delta,logbeta,logtau),
>            start=list(loga40=-3.76,loga30=-2.63,loga20=-1.39,loga0=1.68,
>                       Eg=27000,Ee=200,k=0.16,h=0.47,delta=1.97,logbeta=2.2,logtau=-0.02) )
>
> results in a lot of warnings (my translation into english):
> "In numericDeriv(form[[3L]], names(ind), env) :
> imaginary parts removed during the conversion”
>
> I’ve the same error trying:
> y <- E_2S2P1D(f_data,c(rep(-3.76,5),rep(-2.63,5),rep(-1.39,5),rep(0,5),rep(1.68,5)),27000,200,0.16,0.47,1.97,2.2,0.02)
> plot(E,y)
>
> If I got it right R is not able to handle regression problems on complex-valued functions, correct?
> If yes, is there some workaround?
> For example using MSExcel I write the Real and Imaginary parts separately, and than I minimize
>
> error = sum [ ( Re(E) - Re(E_E_2S2P1D) ) / Re(E) )^2 ] + sum [ ( Im(E) - Im(E_E_2S2P1D) ) / Im(E) )^2 ]
>
> using the MSExcel solver function.
>
> thank you for your help
> Andrea
>
> PS
> Thanks to David Winsemius for his suggestions
>
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