Thank you, Nick for your response. I checked the agreement between the 2
probabilities and found the probabilities not to be as in agreement as
what I thought at first by looking at the pairwise correlation. I think
I should then stick with cloglog or do some more reading on -relogit-.
When I asked about -relogit- being last updated in 1999 I simply was
wondering whether stata had developed something of it's own since then
for analyzing rare events.
Regards,
Bellinda
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of n j cox
Sent: Thursday, January 11, 2007 12:28 PM
To: [email protected]
Subject: Re: st: cloglog or logit
Your results suggest very high correlation between
predicted probabilities from complementary log log
and logit. Just check that there is also very high
agreement. For example, 10x and x are correlated +1
but only at 0 do they agree.
I'm not sure what you infer from the fact that Gary King's program
dates from 1999. Gary was a keen Gauss user, is now a
keen R user. Some of the people in his group have used
Stata heavily. I think that's the main reason -relogit-
has not been updated recently.
Nick
[email protected]
Kallimanis, Bellinda
I have data where my outcome is a rare event, it occurs in 0.97% of my
sample (n =11,618). So I was reading about complimentary log log
regression and thought it may be appropriate, but when I compared the
predicted probabilities of the complimentary log log model and a logit
model I get a pariwise correlation of 0.9991 which suggests to me that
the complimentary log log model isn't doing a better job of predicting
than the logit model. The coefficients are reasonably close to each
other, see output below.
Does this mean I should stick with a logit model and perhaps just alter
the cutoff value? Also I came across the work of Gary King and his
-relogit- command found at http://gking.harvard.edu/stats.shtml#relogit
though I see this was last updated in 1999 so I'm not sure how relevant
it is. Any thoughts would be greatly appreciated.
Regards,
Bellinda
. xi:logit wander age i.psych_state delirium inapprop_beh e1k i.b4
i.g1ea_c
i.psych_state _Ipsych_sta_0-2 (naturally coded; _Ipsych_sta_
omitted)
i.b4 _Ib4_0-2 (naturally coded; _Ib4_0 omitted)
i.g1ea_c _Ig1ea_c_0-2 (naturally coded; _Ig1ea_c_0
omitted)
Logistic regression Number of obs =
11618
LR chi2(10) =
237.38
Prob > chi2 =
0.0000
Log likelihood = -517.27798 Pseudo R2 =
0.1866
------------------------------------------------------------------------
------
wander | Coef. Std. Err. z P>|z| [95%
ConfInterval]
-------------+----------------------------------------------------------
------
age | .0275566 .0087225 3.16 0.002 .0104608
.0446523
_Ipsych_st~1 | .9608209 .2334963 4.11 0.000 .5031765
1.418465
_Ipsych_st~2 | 1.079544 .3496697 3.09 0.002 .394204
1.764884
delirium | .7911933 .2471891 3.20 0.001 .3067116
1.275675
inapprop_b | .9479077 .2576809 3.68 0.000 .4428624
1.452953
e1k | .6659235 .2701732 2.46 0.014 .1363938
1.195453
_Ib4_1 | 1.374957 .2451875 5.61 0.000 .8943987
1.855516
_Ib4_2 | 2.275775 .2786492 8.17 0.000 1.729633
2.821918
_Ig1ea_c_1 | .2038798 .2409538 0.85 0.397 -.268381
.6761407
_Ig1ea_c_2 | -.7956763 .3723895 -2.14 0.033 -1.525546
.0658064
_cons | -7.76677 .6269897 -12.39 0.000 -8.995647
6.537892
------------------------------------------------------------------------
------
xi:cloglog wander age i.psych_state delirium inapprop_beh e1k i.b4
i.g1ea_c
i.psych_state _Ipsych_sta_0-2 (naturally coded; _Ipsych_sta_
omitted)
i.b4 _Ib4_0-2 (naturally coded; _Ib4_0 omitted)
i.g1ea_c _Ig1ea_c_0-2 (naturally coded; _Ig1ea_c_0
omitted)
Complementary log-log regression Number of obs =
11618
Zero outcomes =
11505
Nonzero outcomes = 113
LR chi2(10) =
236.78
Log likelihood = -517.57978 Prob > chi2 =
0.0000
------------------------------------------------------------------------
------
wander | Coef. Std. Err. z P>|z| [95%
ConfInterval]
-------------+----------------------------------------------------------
------
age | .0272044 .0084431 3.22 0.001 .0106562
.0437527
_Ipsych_st~1 | .9297655 .2285607 4.07 0.000 .4817946
1.377736
_Ipsych_st~2 | 1.067526 .3399633 3.14 0.002 .4012106
1.733842
delirium | .751 .2397234 3.13 0.002 .2811507
1.220849
inapprop_b | .8954374 .2476817 3.62 0.000 .4099902
1.380885
e1k | .6169237 .2571763 2.40 0.016 .1128674
1.12098
_Ib4_1 | 1.378481 .2420793 5.69 0.000 .904014
1.852948
_Ib4_2 | 2.235668 .2733994 8.18 0.000 1.699815
2.771521
_Ig1ea_c_1 | .1869587 .2322885 0.80 0.421 -.2683184
.6422358
_Ig1ea_c_2 | -.7917172 .3630383 -2.18 0.029 -1.503259
.0801752
_cons | -7.717735 .6062998 -12.73 0.000 -8.906061
6.52941
------------------------------------------------------------------------
------
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