Look at
?predict.glm

mydata.glm <- glm(formula = Death ~ Temperature, family = binomial(link =
"logit"),
data = mydata)

and see that predict(mydata.glm, type="response") gives the predictions on
the probability scale.

On Mon, May 28, 2012 at 10:16 AM, eddie smith <eddieatr@gmail.com> wrote:

> Hello everyone,
>
> I tried to understand the relationship between temperature and the
> death of an organism by using logistic regression.
> glm(formula = Death ~ Temperature, family = binomial(link = "logit"),
> data = mydata)
>
> Coefficients:
>                           Estimate Std. Error z value Pr(>|z|)
> (Intercept)          -87.9161     7.7987  -11.27   <2e-16 ***
> Temperature        2.9532     0.2616   11.29   <2e-16 ***
>
> >From the above summary, I could understand that log odds of death =
> -87.9161 + 2.9532*Temperature.  Odds=exp(log[odds]).  Probability =
> odds/(1+odds)
>
> Assuming my data is randomly normal distributed with (u=0, standard
> deviation=0.35), and I want to run it for n=10,000, how do I get to
> probability from log odds?
>
> Regards,
> Eddie
>
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> PLEASE do read the posting guide
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> and provide commented, minimal, self-contained, reproducible code.
>

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