Possible to tell if sigmoid function is appropriate in logistic regression/GLM?


I had a question regarding the logistic regression function in R. I was wondering if there's a way to tell if predicted curves from a glm will be reasonably shaped or not, e.g. a nice S-shaped curve opposed to a flattened curve/straight line?

I'm not sure if there are any arguments or values that could analyze and provide information regarding this... I calculated the "boundary" using coefficients and it gives a single value, but even if the value is within acceptable range (for us, between 1-50), the curves sometimes come out to be flat/oddly shaped.

Previously, I've looked at AIC and residual values, but I was running into a similar issue where "reasonable" AIC and residual values generated oddly shaped/flat curves.

Standard code that is used to calculate logistic regression (run over multiple data points to estimate phonetic "boundaries"):

glm.1 <- glm(response~stimuli, family = binomial(link="logit")
value <- -glm.1$coefficients[1]/glm.1$coefficients[2]

Logistic regression is a bit tricky for me to fully understand as I'm still learning about it, so I apologize for any confusion! Any help/suggestions are appreciated, thanks!

This topic was automatically closed 21 days after the last reply. New replies are no longer allowed.

If you have a query related to it or one of the replies, start a new topic and refer back with a link.