![]() # 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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