Displaying 2 results from an estimated 2 matches for "smoketrue".
2006 Aug 03
1
how to use the EV AND condEV from BMA's results?
...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 . . ....
2003 May 03
1
expand.grid
...row.factors(ft)
race smoke
1 white FALSE
2 white TRUE
3 black FALSE
4 black TRUE
5 other FALSE
6 other TRUE
in such a way that they can be directly used in glm:
> glm(ft ~ race + smoke, family=binomial, data = row.factors(ft))
...
Coefficients:
(Intercept) raceblack raceother smokeTRUE
1.841 -1.084 -1.109 -1.116
...
Note that the reference level for race is "white" (the first row).
PS - Obviously, the same analysis is very easy from the original
dataframe (which here is supposed to be missing):
> glm(low ~ race + smoke, family=binomial, d...