My goal is to fit this data to a logistic function (Logistic 3P for example). The y variable represents a proportion, so the maximum possible value is 100%. So I need to specify the asymptote as 100% because the model gives me values up this limit, and this is empirically impossible (attached image).
Thak you for the help.
1 ACCEPTED SOLUTION
Accepted Solutions
If I understand correctly, you need to fit Logistic-2P, because the asymptote is known.
In that case, you need to normalize your data, such that Y is between 0 and 1. In your case, I guess that you should divided your Y by 100.
After that, the Logistic-2P will be available:
Scaling your data should not impact the other two parameter estimates, i.e. the remaining two estimates are same. Unless you want to do some kind of prediction of Y given "cp", then your need to scale back after prediction using the fitted model.
If I understand correctly, you need to fit Logistic-2P, because the asymptote is known.
In that case, you need to normalize your data, such that Y is between 0 and 1. In your case, I guess that you should divided your Y by 100.
After that, the Logistic-2P will be available:
Scaling your data should not impact the other two parameter estimates, i.e. the remaining two estimates are same. Unless you want to do some kind of prediction of Y given "cp", then your need to scale back after prediction using the fitted model.
Thank you so much! I have another doubt related to this kind of analysis.
When I try to compare parameter estimates, I obtain this error:
And when I try to make a custom inverse prediction, the std error in two variables is massive (the others are so good):
Is it possible that these two treatments cannot be modelled? Is there a possible solution? They are the two curves marked with an arrow (yellow and purple).
Thak you for the help to all users!
