Hi,
I want to calculate a geometric mean of estimates in separate regressions.
I have n binomial variables, for which I want to calculate composite indices for combinations of probabilities for different groups.
I do a logistic regression on each of these n variables , and compute margins for a categorial variable (at a type observation)
I want to calculate the geometric mean of the n estimates for each value of the categorial variable - and get a sense of the variation of this derived variable
Then for each level of x4, I want to calculate the product of the margins. My instinct is to try something like (for level 1 of x4)
Which does not work. Calculating the mean is simple, but how would I get a standard error for this composite measure? In the worst one, I have four variables in the composite index.
Can I use nlcom, and how do I reference the different results, or how would I calculate this (if possible)
Grateful for any help!
I want to calculate a geometric mean of estimates in separate regressions.
I have n binomial variables, for which I want to calculate composite indices for combinations of probabilities for different groups.
I do a logistic regression on each of these n variables , and compute margins for a categorial variable (at a type observation)
I want to calculate the geometric mean of the n estimates for each value of the categorial variable - and get a sense of the variation of this derived variable
Code:
probit var1 x1 x2 x3 i.x4 margins i.x4 , at(x1=1 x2=0 x3=21) coeflegend post estimates store v1estimate probit var2 x1 x2 x3 i.x4 margins i.x4 , at(x1=1 x2=0 x3=21) coeflegend post estimates store v2estimate
Code:
nlcom ({v1estimate}_b[1.bransch] *{v2_estimate} _b[1.bransch])^(1/2)
Can I use nlcom, and how do I reference the different results, or how would I calculate this (if possible)
Grateful for any help!
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