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st: Negative probabilities after a margins command for a categorical variable (post logistic model).
From
"Scheetz, Marc" <[email protected]>
To
"[email protected]" <[email protected]>
Subject
st: Negative probabilities after a margins command for a categorical variable (post logistic model).
Date
Mon, 14 Oct 2013 14:02:57 +0000
Dear Listserv,
I am reposting a question from last week in hopes of receiving a response. This is my first content post to the listserv; I appreciate your consideration. Please let me know if I violated any rules for posting.
I am wondering if anyone can help explain the scenario below to me. I am running Stata IC v13.0. I am using the margins command after a multivariate-logistic model with the outcome of "died". I am attempting to characterize the probabilities of death according to each categorical increase of the variable "log2X". The referent category below is 2^0=1. I have modeled the variable as categorical since I lose power due to uneven sample size in some of the categories.
My question is that I receive 95% CIs that have negative margins in 2 of the categories (i.e. 2._at: log2X=1, 4._at:log2X= 3).
Perhaps this is a rudimentary question, but I thought that probabilities calculated from Odds Ratios could not be negative. Is this because it is a probability relative to the referent category? Do you see other errors in my syntax (below)? Sincerely,
Marc Scheetz, PharmD, MSc
. logistic died i.log2X a2_day0 log10_days_to_pos_cx
note: 4.log2X != 0 predicts failure perfectly
4.log2X dropped and 5 obs not used
note: 5.log2X != 0 predicts failure perfectly
5.log2X dropped and 3 obs not used
Logistic regression Number of obs = 83
LR chi2(6) = 18.58
Prob > chi2 = 0.0049
Log likelihood = -35.358908 Pseudo R2 = 0.2081
--------------------------------------------------------------------------------------
died | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]
---------------------+--------------------------------------------------
log2X |
1 | .7903086 .92746 -0.20 0.841 .0792275 7.883466
2 | 6.471551 5.420137 2.23 0.026 1.253427 33.41317
3 | 1.587899 1.492738 0.49 0.623 .2515548 10.02335
4 | 1 (empty)
5 | 1 (empty)
6 | 6.542207 6.159993 1.99 0.046 1.033362 41.41868
|
a2_day0 | 1.075268 .0732118 1.07 0.286 .9409374 1.228775
log10_days_to_pos_cx | 4.854903 3.054503 2.51 0.012 1.41462 16.66177
_cons | .012261 .0189665 -2.85 0.004 .0005913 .2542394
. margins, at(log2X=(0(1)6))
Predictive margins Number of obs = 83
Model VCE : OIM
Expression : Pr(died), predict()
1._at : log2X = 0
2._at : log2X = 1
3._at : log2X = 2
4._at : log2X = 3
5._at : log2X = 4
6._at : log2X = 5
7._at : log2X = 6
------------------------------------------------------------------------------
| Delta-method
| Margin Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------
_at |
1 | .1518137 .0514472 2.95 0.003 .0509791 .2526484
2 | .1259233 .1108515 1.14 0.256 -.0913416 .3431883
3 | .4764486 .1462235 3.26 0.001 .1898558 .7630413
4 | .2137861 .1241819 1.72 0.085 -.0296061 .4571782
5 | . (not estimable)
6 | . (not estimable)
7 | .4787175 .1765856 2.71 0.007 .132616 .824819
------------------------------------------------------------------------------
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