Hi,
You can state a probability p as odds p/(1-p) and vice versa. To get an
odds ratio you need actually two odds. Then you can get the odds ration
of being/having "a" instead of "b" by odds(a)/odds(b), where "b" is the
reference level.
If you fit a logistic regression model (which means that your outcome is
dichotomous) then the estimated coefficients are actually
log(oddsratios) - which you can transform to odds by exp() .
You can use a factor-variable with three levels for race and
treatment-contrasts to get odds ratios for not being white against being
white - make sure, that either your factor has "white" as first level or
specify the contrast with the "base" argument.
If you create 3 dummy variables and involve an intercept in your model
your model will be perfectly collinear - the so called "dummy variable
trap" - you can use an intercept and create two dummies for the
covariate levels you are actually interested in and put this in your
logistic model - the result will be the same as with the treatment
contrasts.
hth.
Bunny, lautloscrew.com schrieb:
HI there,
i know this is a basic question, though i need some help because this
is somewhat away from my current issue, but nevertheless interesting
to me... Lets assume i have some estimated probabilities, say
estimated by a logit model. i know i can also state them as an odds
ratio.
Now i´d like to state these odds ratios as a reference to a specific
outcome of my investigated variable.
for example, if my covariate of interest is race and possible outcomes
are white, black and hispanic, whereas the latter are minorities in my
case - how can i state the odds ratio in such a way that white is the
reference (always 1) and other races' odds ratio are relative to the
reference. e.g. hispanics are 1.5 times more likely to ...
Is creating 3 binary dummies for race the right way ? And if so how
can i go on.
As i said, i know this is rather basic, i am thankful for any links /
references...
thanks in advance !
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