Multinomial Logistic Regression Confidence for Specific Parameter Changes

altug_bayram — Thu, 05 Sep 2024 16:02:46 GMT

Hi,

I have a multinomial logistic regression of 3 classes: 0, 1, and 2 and have associated individual models (via Fit Y by X) for N parameters.

I am now looking into a sensitivity like study in which each parameter is assumed at certain levels (e.g. at min, max etc..). I have probabilities computed at each such parameter level. I like to rank order probability change in each class (0, 1 and 2) due to each of these parameter actions which is also easy.

Obviously each Prob is computed from Lin functions (in this case both functions are linear) - each line w/ specific p value on slopes and intercepts. What is the recommended and/or fastest way to compute a sort of confidence level of probability on the selected parameter ?levels ?

At this point, I have rather scalar data from my initial population - scalar data for each parameter w/ associated formulas of Lin[Y] and Prob[Z] w/ population statistics... I think I should have applied parameter changes to my original population data and then look into confidence interval from distributions of probabilities (as suspect this will be your answer) .. I was hope there may have been some other way -- perhaps an explicit formula - through a script.

Thx in advance for your help.

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Multinomial Logistic Regression Confidence for Specific Parameter Changes