Dear friends,
In R, the help of "bic.glm" tells the difference between postmean(the
posterior mean of each coefficient from model averaging) and
condpostmean(the posterior mean of each coefficient conditional on the
variable being included in the model), But it's still unclear about the
results explanations, and the artile of Rnews in 2005 on BMA still don't
give more detail on it.
Suppose my results of logistic regression analyzed by bic.glm (BMA) as
follows:(dataset is birthwt(MASS) and i include the interaction)
p!=0 EV SD condEV cond SD model 1 model
2 model 3 model 4 model 5
Intercept 100 0.1841 1.2204 0.184 1.220 1.017
1.175 -0.853 -1.057 0.532
age 17.8 -0.0113 0.0285 -0.063 0.036 .
. . . -0.071
lwt 50.0 -0.0079 0.0093 -0.016 0.007 -0.017 -
0.017 . . .
smokeTRUE 9.5 0.0469 0.1798 0.496 0.345 .
.
. . .
ptdTRUE 99.4 1.5161 0.4751 1.526 0.461 1.407
1.596 1.732 1.463 1.608
htTRUE 54.4 0.9477 1.0269 1.742 0.744 1.894
1.930 . . .
uiTRUE 13.3 0.0976 0.2987 0.731 0.453 .
. . . .
ftv 12.3
.1 -0.0257 0.5117 -0.209 2.438 .
.
-0.867 . .
.2+ 0.7470 2.1277 6.081 3.371 .
. 6.024 . .
age.ftv1 33.7 -0.0136 0.0278 -0.040 0.035 . -
0.036 . . .
age.ftv2. 15.9 -0.0340 0.0950 -0.214 0.135 .
. -0.271 . .
smokeTRUE.uiTRUE 2.4 0.0103 0.1209 0.422 0.652 .
.
. . .
nVar 3
4
3 1 2
post prob 0.117
0.086 0.083 0.061 0.044
1. how should I write my final logistic model?
2. Which parameter estimation should be used, condEV OR EV? How should I use
the two different parameter estimations correctly?
Thanks for your precious time!
--
Kind Regards,
Zhi Jie,Zhang
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