Solved – Ordinal logistic regression in Python

categorical datalogitordered-logitpythonstatsmodels

I would like to run an ordinal logistic regression in Python – for a response variable with three levels and with a few explanatory factors. The statsmodels package supports binary logit and multinomial logit (MNLogit) models, but not ordered logit. Since the underlying math is not that different, I wonder if it can be implemented easily using these? (Alternatively, other Python packages that work are appreciated.)

Best Answer

statsmodels now supports Ordinal Regression:

from statsmodels.miscmodels.ordinal_model import OrderedModel

see their documentation here

Related Solutions

Solved – Checking the proportional odds assumption holds in an ordinal logistic regression using polr function

The dependent variable has 8 ordered levels so in the graph to test the proportional odds assumption you should see 8 different symbols for every independent variable. You see only 2 symbols for every independent variable probably because you have chosen a too short interval for the values of the x-axis. If my conjecture is correct, you just need to use a wider interval for the values of the x-axis. Try this code:

par(mfrow=c(1,1))
plot(t, which=1:8, pch=1:8, xlab='logit', main=' ', xlim=range(s[,3:9]))

Solved – CLMM Output interpretation from R Ordinal

These aren't separate models for the linear and quadratic term for tsf; the results you show are for one single model containing both terms. What the results are suggesting is that you need to have the quadratic term rather than just represent/approximate tsf as a linear function only.

Best Answer

Related Solutions

Solved – Checking the proportional odds assumption holds in an ordinal logistic regression using polr function

Solved – CLMM Output interpretation from R Ordinal

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