This is bad statistical practise, Stijn. Knowing average, or fitted
probabilities for mean covariates tells you something you don't really
need to know--for example, if you code males=0 and females=1 and you
have 30% males in your sample, what's the point of determining the
expected probability for someone with gender 0.7?
We have been seduced by linear models into being lazy; there it does not
matter when we take the mean. But with a non-linear model, such as the
logistic, when you take the means is critical. What you may want to do
in the above example is find the expected probability for males and the
expected probability for females. Then if you want to find an overall
mean, take a weighted combination of these two expected probabilities;
with weights 0.3 and 0.7, if you trust your sample, or, for example,
weights 0.5 and 0.5, if you know better. With more than one covariate,
life gets even more interesting if you try and capture interactions!
But this is very easy to do well with the tools we have in Stata. If
you want to read more about this, take a look at,
Chang, I., Gelman, R. and Pagano, M. Corrected group prognostic curves
and summary statistics. Journal of Chronic Diseases 1982; 35 :669--674.
m.p.
Stijn Ruiter wrote:
Dear all,
I tried to make a graph using postgr3, but did not succeed plotting
expected probabilities (estimated with logit) by a specific variable
(say, Z) holding all other variables at the group mean given Z. Using
the x() option followed by rest(grmean) allows to hold the other
variables constant at the group mean given X, but this is not what I
intended to do.
More specifically, I want to plot expected probabilities over time (so,
"postgr3 year") holding all covariates constant at the year-specific
means. Is this somehow possible?
Kind regards,
Stijn
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