| Re: Trouble with MPFITFUN [message #79937] |
Thu, 12 April 2012 05:35 |
Craig Markwardt
Messages: 1869
Registered: November 1996
|
Senior Member |
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On Thursday, April 12, 2012 4:48:44 AM UTC-4, Helder wrote:
> On Thursday, April 12, 2012 7:10:08 AM UTC+2, Craig Markwardt wrote:
>> On Wednesday, April 11, 2012 12:34:23 PM UTC-4, Helder wrote:
>>> Hi,
>>> I've been spending a bit too much time on this and I am wondering what is going wrong here.
>>> I'm trying to fit using a step function broadened by a Gaussian.
>>> The fitting function is:
>>>
>>> FUNCTION GaussStep, X, P
>>> ;Calculate the broadening of a step function with:
>>> ;P[0] = step position
>>> ;P[1] = left value
>>> ;P[2] = right value
>>> ;P[3] = step width
>>> PRINT, P
>>> P[0] = (P[0] > MIN(X)) < MAX(X)
>>> Y = DBLARR(N_ELEMENTS(X))
>>> LowIndeces = WHERE(X LT P[0], CountLow, COMPLEMENT = HighIndeces, NCOMPLEMENT=CountHigh)
>>> IF CountLow GT 0 THEN Y[LowIndeces] = P[1]
>>> IF CountHigh GT 0 THEN Y[HighIndeces] = P[2]
>>> Sigma=P[3]
>>> nPts=10*Sigma+1.0
>>> kernel=DINDGEN(nPts)-(nPts-1)/2.0
>>> kernel=EXP(-kernel^2/(2.*sigma^2))
>>> kernel/=TOTAL(kernel,/DOUBLE)
>>> yconvol = CONVOL(Y,kernel,/EDGE_TRUNCATE)
>>> RETURN, yconvol
>>> END
>>>
>>> To test MPFITFUN I use the following code:
>>> PRO TestFit
>>> xData = DINDGEN(201)
>>> yData = DBLARR(201)+RANDOMU(SEED,201,/DOUBLE)*0.2-0.1
>>> yData[150:200] += 1.0D
>>> StParam = [148D,MIN(yData),MAX(yData),3D]
>>> DataErr = DBLARR(N_ELEMENTS(xData))+0.2D
>>> Results = MPFITFUN('GaussStep', xData,yData, DataErr, StParam, STATUS=status, /quiet)
>>> PLOT, xData, yData
>>> OPLOT, xData, GaussStep(xData,Results), COLOR = 255L
>>> PRINT, 'Final Parameters = ', Results
>>> PRINT, 'Start Parameters = ', StParam
>>> END
>>>
>>> The output shows all the calls of the fitting function. And I find that at the end there is always NO change in the first parameter. Here is an example of the output:
>>>
>>> 148.00000 -0.099990073 1.0994661 3.0000000
>>> 148.00000 -0.099990073 1.0994661 3.0000000
>>> 148.00000 -0.099990071 1.0994661 3.0000000
>>> 148.00000 -0.099990073 1.0994661 3.0000000
>>> 148.00000 -0.099990073 1.0994661 3.0000000
>>> 148.00000 0.0073445709 1.0082363 2.3488363
>>> 148.00000 0.0073445709 1.0082363 2.3488363
>>> 148.00000 0.0073445710 1.0082363 2.3488363
>>> ...
>>> 148.00000 -0.0039705287 0.99188729 2.0999998
>>> 148.00000 -0.0039705257 0.99188729 2.1000000
>>> 148.00000 -0.0039705254 0.99188729 2.1000000
>>> 148.00000 -0.0039705254 0.99188729 2.1000000
>>> Final Parameters = 148.00000 -0.0039705254 0.99188729 2.1000000
>>> Start Parameters = 148.00000 -0.095071379 1.0978406 3.0000000
>>>
>>> Throughout all the fitting procedure the first parameter has never been changed.
>>>
>>> Am I doing something terribly wrong? I generally have no estimates for the errors in the data, therefore I used 0.1. In the example data this is easy to calculate, but the fitting has to be applied to the most different data sets.
>>>
>>> I also tried playing with the XTOL parameter without any success.
>>>
>>> Any tips are appreciated.
>>>
>>> Many thanks,
>>> Helder
>>>
>>> PS: I tried lots of different initial conditions, I tried using "parinfo.fixed" to block the other parameters, ... but at the end I never get any change in P[0]... sigh..
>>>
>>> PSS: The function GaussStep is working fine... I can replot the data in the correct way by moving the parameters by hand.
>>
>> You are getting closer to the right track.
>>
>> If I were you, I would avoid complicated invocations of CONVOL. It looks like you can compute your "smoothed step function" exactly, by using the ERF (formerly ERRORF) function. I've used that before with success.
>>
>> ERF is much better than your convolution because it actually integrates the gaussian, rather than assuming that sampling a gaussian at a few discrete points is sufficient to integrate it.
>>
>> You might also want to play with using PARINFO, and setting the .STEP or .RELSTEP fields. The fitter can get stuck if your peak position and/or step position is between data samples. Set the parameter step size to something close to your data grid sample size.
>>
>> Best wishes,
>> Craig Markwardt
>
> Thanks Craig,
> your tip was very useful. I never thought about the ERF!
> Here is the function I am now using:
...
> So far it worked fine!
> Thanks again.
> I will see what I can do with the Parinfo parameters. I'm currently using the procedure to show live fitting whilst moving a cross section on an image and it seems stable enough... Fits edges also where one would not see one.
I'm glad that it's working. The reason I suggested PARINFO is that the fitter can get stuck. If it tries to make the step too narrow (P[2] -> 0) during its iterations, then it will have a hard time getting out of that condition in later iterations. A step function with width 0.0001x(grid spacing) gives essentially the same chi-square as a step function with 0.00010001x(grid spacing), so the partial derivative goes to zero, or is noisy.
By setting up PARINFO.STEP, you can make sure that MPFIT takes large enough steps for partial derivatives.
Craig
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| Re: Trouble with MPFITFUN [message #79938 is a reply to message #79937] |
Thu, 12 April 2012 01:48  |
Helder Marchetto
Messages: 520
Registered: November 2011
|
Senior Member |
|
|
On Thursday, April 12, 2012 7:10:08 AM UTC+2, Craig Markwardt wrote:
> On Wednesday, April 11, 2012 12:34:23 PM UTC-4, Helder wrote:
>> Hi,
>> I've been spending a bit too much time on this and I am wondering what is going wrong here.
>> I'm trying to fit using a step function broadened by a Gaussian.
>> The fitting function is:
>>
>> FUNCTION GaussStep, X, P
>> ;Calculate the broadening of a step function with:
>> ;P[0] = step position
>> ;P[1] = left value
>> ;P[2] = right value
>> ;P[3] = step width
>> PRINT, P
>> P[0] = (P[0] > MIN(X)) < MAX(X)
>> Y = DBLARR(N_ELEMENTS(X))
>> LowIndeces = WHERE(X LT P[0], CountLow, COMPLEMENT = HighIndeces, NCOMPLEMENT=CountHigh)
>> IF CountLow GT 0 THEN Y[LowIndeces] = P[1]
>> IF CountHigh GT 0 THEN Y[HighIndeces] = P[2]
>> Sigma=P[3]
>> nPts=10*Sigma+1.0
>> kernel=DINDGEN(nPts)-(nPts-1)/2.0
>> kernel=EXP(-kernel^2/(2.*sigma^2))
>> kernel/=TOTAL(kernel,/DOUBLE)
>> yconvol = CONVOL(Y,kernel,/EDGE_TRUNCATE)
>> RETURN, yconvol
>> END
>>
>> To test MPFITFUN I use the following code:
>> PRO TestFit
>> xData = DINDGEN(201)
>> yData = DBLARR(201)+RANDOMU(SEED,201,/DOUBLE)*0.2-0.1
>> yData[150:200] += 1.0D
>> StParam = [148D,MIN(yData),MAX(yData),3D]
>> DataErr = DBLARR(N_ELEMENTS(xData))+0.2D
>> Results = MPFITFUN('GaussStep', xData,yData, DataErr, StParam, STATUS=status, /quiet)
>> PLOT, xData, yData
>> OPLOT, xData, GaussStep(xData,Results), COLOR = 255L
>> PRINT, 'Final Parameters = ', Results
>> PRINT, 'Start Parameters = ', StParam
>> END
>>
>> The output shows all the calls of the fitting function. And I find that at the end there is always NO change in the first parameter. Here is an example of the output:
>>
>> 148.00000 -0.099990073 1.0994661 3.0000000
>> 148.00000 -0.099990073 1.0994661 3.0000000
>> 148.00000 -0.099990071 1.0994661 3.0000000
>> 148.00000 -0.099990073 1.0994661 3.0000000
>> 148.00000 -0.099990073 1.0994661 3.0000000
>> 148.00000 0.0073445709 1.0082363 2.3488363
>> 148.00000 0.0073445709 1.0082363 2.3488363
>> 148.00000 0.0073445710 1.0082363 2.3488363
>> ...
>> 148.00000 -0.0039705287 0.99188729 2.0999998
>> 148.00000 -0.0039705257 0.99188729 2.1000000
>> 148.00000 -0.0039705254 0.99188729 2.1000000
>> 148.00000 -0.0039705254 0.99188729 2.1000000
>> Final Parameters = 148.00000 -0.0039705254 0.99188729 2.1000000
>> Start Parameters = 148.00000 -0.095071379 1.0978406 3.0000000
>>
>> Throughout all the fitting procedure the first parameter has never been changed.
>>
>> Am I doing something terribly wrong? I generally have no estimates for the errors in the data, therefore I used 0.1. In the example data this is easy to calculate, but the fitting has to be applied to the most different data sets.
>>
>> I also tried playing with the XTOL parameter without any success.
>>
>> Any tips are appreciated.
>>
>> Many thanks,
>> Helder
>>
>> PS: I tried lots of different initial conditions, I tried using "parinfo.fixed" to block the other parameters, ... but at the end I never get any change in P[0]... sigh..
>>
>> PSS: The function GaussStep is working fine... I can replot the data in the correct way by moving the parameters by hand.
>
> You are getting closer to the right track.
>
> If I were you, I would avoid complicated invocations of CONVOL. It looks like you can compute your "smoothed step function" exactly, by using the ERF (formerly ERRORF) function. I've used that before with success.
>
> ERF is much better than your convolution because it actually integrates the gaussian, rather than assuming that sampling a gaussian at a few discrete points is sufficient to integrate it.
>
> You might also want to play with using PARINFO, and setting the .STEP or .RELSTEP fields. The fitter can get stuck if your peak position and/or step position is between data samples. Set the parameter step size to something close to your data grid sample size.
>
> Best wishes,
> Craig Markwardt
Thanks Craig,
your tip was very useful. I never thought about the ERF!
Here is the function I am now using:
FUNCTION StepErrFun, X, P
;Parameter definition
;P[0] = Step height
;P[1] = Location of step
;P[2] = 2*SQRT(ALOG(P[2])) is the FWHM
;P[3] = Step offset
RETURN,(P[0]/2D)* ERFC((P[1]-X)/P[2])+P[3]
END
So far it worked fine!
Thanks again.
I will see what I can do with the Parinfo parameters. I'm currently using the procedure to show live fitting whilst moving a cross section on an image and it seems stable enough... Fits edges also where one would not see one.
Regards,
Helder
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| Re: Trouble with MPFITFUN [message #79940 is a reply to message #79938] |
Wed, 11 April 2012 22:10 |
Craig Markwardt
Messages: 1869
Registered: November 1996
|
Senior Member |
|
|
On Wednesday, April 11, 2012 12:34:23 PM UTC-4, Helder wrote:
> Hi,
> I've been spending a bit too much time on this and I am wondering what is going wrong here.
> I'm trying to fit using a step function broadened by a Gaussian.
> The fitting function is:
>
> FUNCTION GaussStep, X, P
> ;Calculate the broadening of a step function with:
> ;P[0] = step position
> ;P[1] = left value
> ;P[2] = right value
> ;P[3] = step width
> PRINT, P
> P[0] = (P[0] > MIN(X)) < MAX(X)
> Y = DBLARR(N_ELEMENTS(X))
> LowIndeces = WHERE(X LT P[0], CountLow, COMPLEMENT = HighIndeces, NCOMPLEMENT=CountHigh)
> IF CountLow GT 0 THEN Y[LowIndeces] = P[1]
> IF CountHigh GT 0 THEN Y[HighIndeces] = P[2]
> Sigma=P[3]
> nPts=10*Sigma+1.0
> kernel=DINDGEN(nPts)-(nPts-1)/2.0
> kernel=EXP(-kernel^2/(2.*sigma^2))
> kernel/=TOTAL(kernel,/DOUBLE)
> yconvol = CONVOL(Y,kernel,/EDGE_TRUNCATE)
> RETURN, yconvol
> END
>
> To test MPFITFUN I use the following code:
> PRO TestFit
> xData = DINDGEN(201)
> yData = DBLARR(201)+RANDOMU(SEED,201,/DOUBLE)*0.2-0.1
> yData[150:200] += 1.0D
> StParam = [148D,MIN(yData),MAX(yData),3D]
> DataErr = DBLARR(N_ELEMENTS(xData))+0.2D
> Results = MPFITFUN('GaussStep', xData,yData, DataErr, StParam, STATUS=status, /quiet)
> PLOT, xData, yData
> OPLOT, xData, GaussStep(xData,Results), COLOR = 255L
> PRINT, 'Final Parameters = ', Results
> PRINT, 'Start Parameters = ', StParam
> END
>
> The output shows all the calls of the fitting function. And I find that at the end there is always NO change in the first parameter. Here is an example of the output:
>
> 148.00000 -0.099990073 1.0994661 3.0000000
> 148.00000 -0.099990073 1.0994661 3.0000000
> 148.00000 -0.099990071 1.0994661 3.0000000
> 148.00000 -0.099990073 1.0994661 3.0000000
> 148.00000 -0.099990073 1.0994661 3.0000000
> 148.00000 0.0073445709 1.0082363 2.3488363
> 148.00000 0.0073445709 1.0082363 2.3488363
> 148.00000 0.0073445710 1.0082363 2.3488363
> ...
> 148.00000 -0.0039705287 0.99188729 2.0999998
> 148.00000 -0.0039705257 0.99188729 2.1000000
> 148.00000 -0.0039705254 0.99188729 2.1000000
> 148.00000 -0.0039705254 0.99188729 2.1000000
> Final Parameters = 148.00000 -0.0039705254 0.99188729 2.1000000
> Start Parameters = 148.00000 -0.095071379 1.0978406 3.0000000
>
> Throughout all the fitting procedure the first parameter has never been changed.
>
> Am I doing something terribly wrong? I generally have no estimates for the errors in the data, therefore I used 0.1. In the example data this is easy to calculate, but the fitting has to be applied to the most different data sets.
>
> I also tried playing with the XTOL parameter without any success.
>
> Any tips are appreciated.
>
> Many thanks,
> Helder
>
> PS: I tried lots of different initial conditions, I tried using "parinfo.fixed" to block the other parameters, ... but at the end I never get any change in P[0]... sigh..
>
> PSS: The function GaussStep is working fine... I can replot the data in the correct way by moving the parameters by hand.
You are getting closer to the right track.
If I were you, I would avoid complicated invocations of CONVOL. It looks like you can compute your "smoothed step function" exactly, by using the ERF (formerly ERRORF) function. I've used that before with success.
ERF is much better than your convolution because it actually integrates the gaussian, rather than assuming that sampling a gaussian at a few discrete points is sufficient to integrate it.
You might also want to play with using PARINFO, and setting the .STEP or .RELSTEP fields. The fitter can get stuck if your peak position and/or step position is between data samples. Set the parameter step size to something close to your data grid sample size.
Best wishes,
Craig Markwardt
|
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| Re: Trouble with MPFITFUN [message #79941 is a reply to message #79940] |
Wed, 11 April 2012 12:55 |
David Fanning
Messages: 11724
Registered: August 2001
|
Senior Member |
|
|
Helder writes:
> Ok, even dessert helped. By doing a smooth+deriv operations and then fitting the peak, I can get quite reliably a peak.
> I think I'll try to delete this post tomorrow and insert it in the hall of shame...
Coyote was telling me they have pretty much run out
of space in the current location. They are looking
for an airport hanger, apparently, but won't be
accepting new donations until they find something
suitable. :-(
Cheers,
David
--
David Fanning, Ph.D.
Fanning Software Consulting, Inc.
Coyote's Guide to IDL Programming: http://www.idlcoyote.com/
Sepore ma de ni thui. ("Perhaps thou speakest truth.")
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| Re: Trouble with MPFITFUN [message #79942 is a reply to message #79941] |
Wed, 11 April 2012 12:51 |
Helder Marchetto
Messages: 520
Registered: November 2011
|
Senior Member |
|
|
On Wednesday, April 11, 2012 8:08:31 PM UTC+2, Helder wrote:
> On Wednesday, April 11, 2012 6:34:23 PM UTC+2, Helder wrote:
>> Hi,
>> I've been spending a bit too much time on this and I am wondering what is going wrong here.
>> I'm trying to fit using a step function broadened by a Gaussian.
>> The fitting function is:
>>
>> FUNCTION GaussStep, X, P
>> ;Calculate the broadening of a step function with:
>> ;P[0] = step position
>> ;P[1] = left value
>> ;P[2] = right value
>> ;P[3] = step width
>> PRINT, P
>> P[0] = (P[0] > MIN(X)) < MAX(X)
>> Y = DBLARR(N_ELEMENTS(X))
>> LowIndeces = WHERE(X LT P[0], CountLow, COMPLEMENT = HighIndeces, NCOMPLEMENT=CountHigh)
>> IF CountLow GT 0 THEN Y[LowIndeces] = P[1]
>> IF CountHigh GT 0 THEN Y[HighIndeces] = P[2]
>> Sigma=P[3]
>> nPts=10*Sigma+1.0
>> kernel=DINDGEN(nPts)-(nPts-1)/2.0
>> kernel=EXP(-kernel^2/(2.*sigma^2))
>> kernel/=TOTAL(kernel,/DOUBLE)
>> yconvol = CONVOL(Y,kernel,/EDGE_TRUNCATE)
>> RETURN, yconvol
>> END
>>
>> To test MPFITFUN I use the following code:
>> PRO TestFit
>> xData = DINDGEN(201)
>> yData = DBLARR(201)+RANDOMU(SEED,201,/DOUBLE)*0.2-0.1
>> yData[150:200] += 1.0D
>> StParam = [148D,MIN(yData),MAX(yData),3D]
>> DataErr = DBLARR(N_ELEMENTS(xData))+0.2D
>> Results = MPFITFUN('GaussStep', xData,yData, DataErr, StParam, STATUS=status, /quiet)
>> PLOT, xData, yData
>> OPLOT, xData, GaussStep(xData,Results), COLOR = 255L
>> PRINT, 'Final Parameters = ', Results
>> PRINT, 'Start Parameters = ', StParam
>> END
>>
>> The output shows all the calls of the fitting function. And I find that at the end there is always NO change in the first parameter. Here is an example of the output:
>>
>> 148.00000 -0.099990073 1.0994661 3.0000000
>> 148.00000 -0.099990073 1.0994661 3.0000000
>> 148.00000 -0.099990071 1.0994661 3.0000000
>> 148.00000 -0.099990073 1.0994661 3.0000000
>> 148.00000 -0.099990073 1.0994661 3.0000000
>> 148.00000 0.0073445709 1.0082363 2.3488363
>> 148.00000 0.0073445709 1.0082363 2.3488363
>> 148.00000 0.0073445710 1.0082363 2.3488363
>> ...
>> 148.00000 -0.0039705287 0.99188729 2.0999998
>> 148.00000 -0.0039705257 0.99188729 2.1000000
>> 148.00000 -0.0039705254 0.99188729 2.1000000
>> 148.00000 -0.0039705254 0.99188729 2.1000000
>> Final Parameters = 148.00000 -0.0039705254 0.99188729 2.1000000
>> Start Parameters = 148.00000 -0.095071379 1.0978406 3.0000000
>>
>> Throughout all the fitting procedure the first parameter has never been changed.
>>
>> Am I doing something terribly wrong? I generally have no estimates for the errors in the data, therefore I used 0.1. In the example data this is easy to calculate, but the fitting has to be applied to the most different data sets.
>>
>> I also tried playing with the XTOL parameter without any success.
>>
>> Any tips are appreciated.
>>
>> Many thanks,
>> Helder
>>
>> PS: I tried lots of different initial conditions, I tried using "parinfo.fixed" to block the other parameters, ... but at the end I never get any change in P[0]... sigh..
>>
>> PSS: The function GaussStep is working fine... I can replot the data in the correct way by moving the parameters by hand.
>
> Ok, just had some dinner and the head is a bit clearer now. I think everything is working fine. The only problem is that the square residuals don't change a lot when changing the position of the step. It's like finding the minimum on a flat 100 square meter surface with a little 1 square cm hole hidden somewhere...
>
> I think this is it, if anybody has a suggestion on how to better estimate the step, that would be appreciated... I'll go for dessert and maybe get a good idea on how to do that.
>
> Cheers,
> Helder
Ok, even dessert helped. By doing a smooth+deriv operations and then fitting the peak, I can get quite reliably a peak.
I think I'll try to delete this post tomorrow and insert it in the hall of shame...
Cheers, h
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| Re: Trouble with MPFITFUN [message #79944 is a reply to message #79942] |
Wed, 11 April 2012 11:08 |
Helder Marchetto
Messages: 520
Registered: November 2011
|
Senior Member |
|
|
On Wednesday, April 11, 2012 6:34:23 PM UTC+2, Helder wrote:
> Hi,
> I've been spending a bit too much time on this and I am wondering what is going wrong here.
> I'm trying to fit using a step function broadened by a Gaussian.
> The fitting function is:
>
> FUNCTION GaussStep, X, P
> ;Calculate the broadening of a step function with:
> ;P[0] = step position
> ;P[1] = left value
> ;P[2] = right value
> ;P[3] = step width
> PRINT, P
> P[0] = (P[0] > MIN(X)) < MAX(X)
> Y = DBLARR(N_ELEMENTS(X))
> LowIndeces = WHERE(X LT P[0], CountLow, COMPLEMENT = HighIndeces, NCOMPLEMENT=CountHigh)
> IF CountLow GT 0 THEN Y[LowIndeces] = P[1]
> IF CountHigh GT 0 THEN Y[HighIndeces] = P[2]
> Sigma=P[3]
> nPts=10*Sigma+1.0
> kernel=DINDGEN(nPts)-(nPts-1)/2.0
> kernel=EXP(-kernel^2/(2.*sigma^2))
> kernel/=TOTAL(kernel,/DOUBLE)
> yconvol = CONVOL(Y,kernel,/EDGE_TRUNCATE)
> RETURN, yconvol
> END
>
> To test MPFITFUN I use the following code:
> PRO TestFit
> xData = DINDGEN(201)
> yData = DBLARR(201)+RANDOMU(SEED,201,/DOUBLE)*0.2-0.1
> yData[150:200] += 1.0D
> StParam = [148D,MIN(yData),MAX(yData),3D]
> DataErr = DBLARR(N_ELEMENTS(xData))+0.2D
> Results = MPFITFUN('GaussStep', xData,yData, DataErr, StParam, STATUS=status, /quiet)
> PLOT, xData, yData
> OPLOT, xData, GaussStep(xData,Results), COLOR = 255L
> PRINT, 'Final Parameters = ', Results
> PRINT, 'Start Parameters = ', StParam
> END
>
> The output shows all the calls of the fitting function. And I find that at the end there is always NO change in the first parameter. Here is an example of the output:
>
> 148.00000 -0.099990073 1.0994661 3.0000000
> 148.00000 -0.099990073 1.0994661 3.0000000
> 148.00000 -0.099990071 1.0994661 3.0000000
> 148.00000 -0.099990073 1.0994661 3.0000000
> 148.00000 -0.099990073 1.0994661 3.0000000
> 148.00000 0.0073445709 1.0082363 2.3488363
> 148.00000 0.0073445709 1.0082363 2.3488363
> 148.00000 0.0073445710 1.0082363 2.3488363
> ...
> 148.00000 -0.0039705287 0.99188729 2.0999998
> 148.00000 -0.0039705257 0.99188729 2.1000000
> 148.00000 -0.0039705254 0.99188729 2.1000000
> 148.00000 -0.0039705254 0.99188729 2.1000000
> Final Parameters = 148.00000 -0.0039705254 0.99188729 2.1000000
> Start Parameters = 148.00000 -0.095071379 1.0978406 3.0000000
>
> Throughout all the fitting procedure the first parameter has never been changed.
>
> Am I doing something terribly wrong? I generally have no estimates for the errors in the data, therefore I used 0.1. In the example data this is easy to calculate, but the fitting has to be applied to the most different data sets.
>
> I also tried playing with the XTOL parameter without any success.
>
> Any tips are appreciated.
>
> Many thanks,
> Helder
>
> PS: I tried lots of different initial conditions, I tried using "parinfo.fixed" to block the other parameters, ... but at the end I never get any change in P[0]... sigh..
>
> PSS: The function GaussStep is working fine... I can replot the data in the correct way by moving the parameters by hand.
Ok, just had some dinner and the head is a bit clearer now. I think everything is working fine. The only problem is that the square residuals don't change a lot when changing the position of the step. It's like finding the minimum on a flat 100 square meter surface with a little 1 square cm hole hidden somewhere...
I think this is it, if anybody has a suggestion on how to better estimate the step, that would be appreciated... I'll go for dessert and maybe get a good idea on how to do that.
Cheers,
Helder
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