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Re: st: Marginsplot on backtransformed data
From
Richard Williams <[email protected]>
To
[email protected], [email protected]
Subject
Re: st: Marginsplot on backtransformed data
Date
Thu, 19 Dec 2013 11:13:58 -0500
At 10:50 AM 12/19/2013, Scott Merryman wrote:
One could also use the -expression()- option in -margins-
margins race, expression(predict(xb)^2)
marginsplot, name(regress2,replace)
Good point. I've used the expression option to do thing like multiply
numbers by 100 so you get 37.3 instead of .373.
That still leaves open the question of whether you should use regress
(computing the square root of the dv yourself) or use glm (using the
power link.) In my example it doesn't make too much difference. In
general is it better to use glm or are there pros and cons of each approach?
Scott
On Thu, Dec 19, 2013 at 9:30 AM, Richard Williams
<[email protected]> wrote:
> Patrick Royston's -marginscontplot- (available from SSC) can be used when
> you've done a log or other transformation of an independent variable. See
> the help file example entitled "Example using a log-transformed covariate".
>
> For a dependent variable, I think you can use the glm command, at
least some
> of the time. You should get a 2nd opinion on this, e.g. Austin Nichols is
> much better with these sorts of things than I am. When the dependent
> variable has been transformed I believe it is often better to use glm
> anyway. In the following you don't get exactly the same results
from regress
> and glm but I don't think you are supposed to (and the results
are similar).
>
> webuse nhanes2f, clear
> gen sqweight = weight ^.5
> reg sqweight i.race
> margins race
> marginsplot, name(regress)
> glm weight i.race, link(power .5)
> margins race
> marginsplot, name(glm)
>
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Richard Williams, Notre Dame Dept of Sociology
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