That is not a form of the logistic model that I am familiar with. The logistic model is usually formulated as Pr(y=1) = 1/(1+exp(-x*beta)), where y is a binary response variable where y=1 is the event level, x is the vector of predictor values and beta is the vector of parameters to be estimated. Many goodness of fit tests and measures and R-square statistics are also available in that procedure. You can easily fit this model in PROC LOGISTIC. See the examples in the PROC LOGISTIC documentation, but here is a simple example for a single predictor that produces goodness of fit statistics:
proc logistic;
model y(event="1") = x / gof;
run;
The model presented from 10 years ago is a fairly standard parameterization of the 3 parameter logistic growth model, with asymptotes at 0 (min) and k (max). A common use is plant height as a function of about anything, actually - water availability, fertilizer applied, etc.
SteveDenham
Fitting the so-called 4- and 5-parameter logistic models of similar form is discussed in this note. The method shown there using PROC NLMIXED could be modified appropriately to fit the requested model form.
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