Thank you very much for your time and thorough answers, Dieter Menne and Daniel Malter.
It has to be said that I lack some of the basic statistical background and don´t have a "gut feeling" if the tings I do is correct or not. Also, I use SigmaPlot for the fitting, since I am not that familiar with R. The reason that I choose the R help forum is that I know R is highly regarded among statisticians and I probably will, in the future, use R in stead of SigmaPlot. -I forgot to mention what equation I use for fitting: y0+a*(1-exp(-b*x)), and for the halftime calculation I use: ln(2)/b 1. I have tried fitting a double exponential equation ( y0+a*(1-exp(-b*x))+c*(1-exp(-d*x)) ) and that of course give a better fit, but it makes the the biological interpretations more difficult. Is it then possible to separate the two fraction/ parts (a fast diffusing and a slow diffusing component, in my example) to modell the curves independent of each other? (for calculation of halftime etc). 2. In SigmaPlot I get the R, Rsqr (in my example figure in the first post those are 0,983 and 0,967 respectively) and I wonder if these values is enough for evaluation of the fit? I have seen that chi-square previously has been used for this in FRAP literature and "Probability Q tells you if the chi-square calculated from the fitting results are reasonably within the range of possible measurement errors. Q > 0.1 can be considered a good fit, Q > 0.01 is a moderately good fit, and Q < 0.01 recommends you either to think about different model equation or..." (Igor Pro manual). Then what is the relation between Rsqr and chi-square, and is there a general threshold for a good/bad fit? 3. If you have 12 independent examples (equal x-value (time)) should you: a, fit all single experiments and find the average halftime, or b, calculate the average value for each time-point and base the fit on the average - and then find the halftime, or c, import all the curves in SigmaPlot and do the fitting based on multiple values and best fit for each time-point? What will the outcome be in these different approaches? I know my questions may seem trivial for you, but I really appreciate some constructive feedback. Thank you in advance! Daniel Malter wrote: > > With that you should probably get advice from your local stats department. > Although you describe your procedure, we do not know your data. And in > particular, we do not know what you do in R. > > Just from inspecting your graph, it looks that your estimated function > undershoots/overshoots the fitted values systematically for certain > intervals of the fit. For example, over the entire last part of the fitted > curve, the actual data points lie predominantly above the fitted curve and > for a long interval before that they lie predominantly below the fitted > curve. This should not be so, which indicates that your fitted function, > despite its relative fit, may not reflect your data generating process > well. > > Regarding fixing the function in the first observation/data point: That's > wrong. This point would then carry an infinitely greater amount of > information than all the other points (because you assume zero error for > this point). Just imagine you would have a second point like this > somewhere > else on the timeline. Then you could perfectly fit your nonlinear function > with two data points. You could only do that if your first point is > nonstochastic, i.e. if there is no error and you would get the EXACT same > value at that point in time every time you run your experiment. > > Again, I think it's a question the definition of your function. > > Best, > Daniel > > ------------------------- > cuncta stricte discussurus > ------------------------- > > -----Ursprüngliche Nachricht----- > Von: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] Im > Auftrag von LuriFax > Gesendet: Tuesday, September 02, 2008 8:06 AM > An: r-help@r-project.org > Betreff: [R] Help with nonlinear regressional > > > Dear All, > > I am doing experiments in live plant tissue using a laser confocal > microscope. The method is called "fluorescence recovery after > photo-bleaching" (FRAP) and here follows a short summary: > > 1. Record/ measure fluorescence intensity in a defined, round region of > interest (ROI, in this case a small spot) to determine the initial > intensity > value before the bleaching. This pre-bleach value is also used for > normalising the curve (pre-bleach is then set to 1). > > 2. Bleach this ROI (with high laser intensity). > > 3. Record/ measure the recovery of fluorescence over time in the ROI until > it reaches a steady state (a plateau). > . > n. Fit the measured intensity for each time point and mesure the half time > (the timepoint which the curve has reached half the plateau), and more... > > The recovery of fluorescence in the ROI is used as a measurement of > protein > diffusion in the time range of the experiment. A steep curve means that > the > molecules has diffused rapidly into the observed ROI and vice versa. > > > > When I do a regressional curve fit without any constraints I get a huge > deviation from the measured value and the fitted curve at the first data > point in the curve (se the bottom picture). > > My question is simply: can I constrain the fitting so that the first point > used in fitting is equal to the measured first point? Also, is this method > of fitting statistically justified / a correct way of doing it when it > comes > to statistical error? > > Since the first point in the curve is critical for the calculation of the > halftime I get a substantial deviation when I compare the halftime from a > "automatically" fitted curve (let software decide) and a fitting with a > constrained first-point (y0). > > I assume that all measured values have the same amount of noise and > therefore it seems strange that the first residual deviates that strongly > (the curve fit is even not in the range of the standard deviation of the > first point). > > > I will greatly appreciate some feedback. Thank you. > > ----------------------- > http://www.nabble.com/file/p19268931/CurveFit_SigmaPlot.png > -- > View this message in context: > http://www.nabble.com/Help-with-nonlinear-regressional-tp19268931p19268931.h > tml > Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > R-help@r-project.org mailing list > 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@r-project.org mailing list > 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. > > -- View this message in context: http://www.nabble.com/Help-with-nonlinear-regressional-tp19268931p19291976.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list 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.
