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# the layout function is another way to subdivide the plotting area # we now plot the data linearly and logarithmically Then we fit a logistic model and as a small bonus, the Baranyi growth model with an explicit lag phase. See the following example, where we use heuristics to derive start values ourselves instead of specifying them manually. Use of SSLogis is a good idea, as you don't need to specify start values, but a definition of an own function is more flexible. Please could someone confirm whether these two steps are the best way to go about it? Or should I use values that I have extracted from previous similar data for the starting values?Īdditionally, what should I do if I don't want the logistic function to be defined by the asymptote at all?Īs Grothendieck writes, there is no general "best way", it always depends on you particular aims. However somewhere else I also read that you should use the SSlogis function for fitting a logistic function. where you clearly need the starting values to find the best-fitting values (?).Īnd then this post explains that to get the starting values, you can use a "selfstarting model can estimate good starting values for you, so you don't have to specify them": fit <- nls(y ~ SSlogis(x, Asym, xmid, scal), data = ame(x, y)) # get the coefficients using the coef function This tutorial explains that you should use the nls() function like this: fitmodel <- nls(y~a/(1 + exp(-b * (x-c))), start=list(a=1,b=.5,c=25)) I have found some methods online, but I'm not sure which is the correct option. I have data that follows a sigmoid curve and I would like fit a logistic function to extract the three (or two) parameters for each participant.
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