[R] Calculating the probability for a logistic regression
Ben Bolker
bbolker at gmail.com
Wed Nov 30 03:22:34 CET 2011
sirilkt <jankee2010 <at> hotmail.com> writes:
>
> Hi All,
>
> When we run the command : summary ( newmod<-gam(Dlq~ formula,family,,data) )
>
> in R, the output would the effect of smoothness in R.
>
> As of now to calculate the probability I am following the below approach:
>
> 1) Run the plot of the GAM , interpret the curves
>
> 2) Re Run the Regression as a GLM after taking into account the non linear
> terms in step1
>
> 3) Calculate the probability from the coefficients obtained in step2, using
> the appropriate link function
>
> But I came across a paper by SAS (
> http://support.sas.com/rnd/app/papers/gams.pdf ), Where the parameters
> outputs are also given when the program is run.
>
> So I was wondering if we have something similar in R also? I tried hard but
> could not find anything.
It's still not entirely clear what you want to do.
What's wrong with
library(gam)
data(kyphosis)
gg <- gam(Kyphosis ~ s(Age,3) +
s(Start,3) + s(Number,3),
data=kyphosis, family=binomial)
predict(gg,type="response")
?
See ?predict.gam for more details.
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