Thanks for your response Nick. In a nutshell, age is not linear in the
logit. I'm using the fracpoly command to identify the best functional
form for age in the full model. The result returned from Fracpoly was a
quadratic function with powers 3 3 (which also looks good with
fracplot). However, when I further assessed the model using the Boxtid
command, the results with the new age transformation - the results were
not favorable (the Ho was rejected). When I transformed another
continuous variable in the same full logistic model (quadratic with
powers 1 2 by Fracpoly), the Boxtid results were favorable, all graphs
looked very good, and the diagnostics were good (linktest, etc...). I'm
trying to understand why my results aren't consistent (Fracpoly and
Boxtid) with the age variable, but is with all other continuous variables?
Nick Cox wrote:
I am not clear what you think Statalist members know
that can help you here. For example, the field
in which you are working, what the response variable
-dmcat- means, and what other predictors there may be are all
hidden from view, so the chance of giving opinions
drawing on substantive expertise is zero. Otherwise
put, you appear to be assuming that the choices
here can all be made on purely statistical criteria,
an attitude which always worries me greatly.
What I have observed, as a kind of anthropologist of
statistical science, is that age plays very different
roles in different fields. Economists often seem
to find that a quadratic in age does very nicely,
whereas biostatisticians often seem to need
more complicated representations, which seems
perfectly plausible given the complexities of
childhood, adolescence, etc.
Either way, -fracpoly- like other programs has
no inbuilt sensor (or censor) selecting theoretically or
scientifically sensible functional forms. So,
I suggest that you plot the curve implied against
age and think about it as something that needs justification
or interpretation independently from the data.
Nick
[email protected]
Suzy
I am trying to transform one final continuous independent
variable (age)
in a logistic regression model. I've tried what I know that's
available
via Stata. For example, I used the fracpoly command and the best
transformation was a second order polynomial with powers 3 3.
Fractional polynomial model comparisons:
---------------------------------------------------------------
age df Deviance Gain P(term) Powers
---------------------------------------------------------------
Not in model 0 2098.129 -- --
Linear 1 1834.224 0.000 0.000 1
m = 1 2 1805.957 28.267 0.000 -1
m = 2 4 1791.327 42.897 0.001 3 3
m = 3 6 1790.526 43.699 0.670 -2 3 3
m = 4 8 1788.431 45.793 0.351 -2 -2 3 3
---------------------------------------------------------------
I then used fracgen to generate the new age variables - age_1
and age_2.
fracgen age 3 3
-> gen double age_1 = X^3
-> gen double age_2 = X^3*ln(X)
(where: X = (age+1)/10)
The coefficients for age_1 and age_2 from the full logistic
regression
model:
--------------------------------------------------------------
----------------
Y var | Odds Ratio Std. Err. z P>|z| [95% Conf.
Interval]
-------------+------------------------------------------------
----------------
age_1 | 1.087994 .0093302 9.83 0.000 1.06986
1.106436
age_2 | .9644247 .0037538 -9.31 0.000 .9570955
.9718101
However the boxtid command rejected the null for both age_1
and age_2....
age_1 | .0100805 .0007172 14.055 Nonlin. dev.
24.646 (P
= 0.000)
p1 | .0535714 .2122906 0.252
--------------------------------------------------------------
----------------
age_2 | -.0021756 .0004885 -4.453 Nonlin. dev.
7.894 (P
= 0.005)
p1 | 3.864227 2.133377 1.811
In all other respects, the preliminary diagnostics look good...
Linktest:
--------------------------------------------------------------
----------------
dmcat | Coef. Std. Err. z P>|z| [95% Conf.
Interval]
-------------+------------------------------------------------
----------------
_hat | .8900851 .1153855 7.71 0.000 .6639337
1.116236
_hatsq | -.0319886 .0307101 -1.04 0.298 -.0921793
.0282022
_cons | -.0450195 .1069617 -0.42 0.674 -.2546606
.1646215
--------------------------------------------------------------
----------------
lroc
Logistic model for dmcat
number of observations = 3354
area under ROC curve = 0.8647
etc...etc...etc...
My question is should I be concerned with the results of the Boxtid
command? Is there something I've done incorrectly or something else I
can do/should do?
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