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Re: st: Calculating predicted probabilities at a given value of one of the independent variables
From
Richard Williams <[email protected]>
To
[email protected], <[email protected]>
Subject
Re: st: Calculating predicted probabilities at a given value of one of the independent variables
Date
Wed, 26 Oct 2011 19:56:51 -0500
At 06:06 PM 10/26/2011, Maria Ana Vitorino wrote:
Dear Statalist users,
I would like to calculate some predicted probabilities after fitting a
multinomial logit model (asclogit). But, the twist is that I would
like to calculate these fitted values for a given level of one of the
independent variables. Namely, I would like to set one of the Xs to
zero when computing the predicted probability (but not in the
estimation).
What would be the best way of going about this? Would it make sense to
do it in 2 steps? In the first step use "predict xb" and in the
second step remove the component of xb that I'm not interested and
calculate the probability using "gen" and the logit formula?
Or is there a better way?
How about something like this?
webuse sysdsn1, clear
clonevar xmale = male
mlogit insure age i.xmale nonwhite i.site
predict p1 p2 p3
replace xmale = 0
predict pp1 pp2 pp3
In other words, clone the variable of interest (or otherwise make
sure that you don't zap the original values), run your model, and
then change the value of the cloned variable to whatever you want.
Depending on what exactly it is you want (e.g. do you need all the
individual predictions, or just some overall average prediction?) you
might be able to do this with margins as well.
Incidentally, I'd be hesitant to say there is some component of xb I
am not interested in. On the other hand, I might be interested in the
predicted probabilities when x is equal to some value. In this case I
am setting the gender variable equal to female.
-------------------------------------------
Richard Williams, Notre Dame Dept of Sociology
OFFICE: (574)631-6668, (574)631-6463
HOME: (574)289-5227
EMAIL: [email protected]
WWW: http://www.nd.edu/~rwilliam
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