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...probability with se.fit=T argument in predict(), so would it make sense to get standard error for posterior probability from lda() and how? Another question about standard error estimate from glm(): is it ok to calculate 95% CI for the predicted probability using the standard error based on normal apprximation, i.e. predicted_probability +/- 1.96 * standard_error? Thanks John [[alternative HTML version deleted]]

...011-05-02 09:46:11 7608.0 23  2011-05-02 09:51:18 7611.5 24  2011-05-02 09:56:20 7605.5 25  2011-05-02 10:01:20 7601.5 I want to approximate this data (actually I dont care, whether keep the time information, or lose it, while making it a function) With approxfun( ), it seems, like I managed to apprximate a function. f <- approxfun(2:nrow(CB), CB[2:nrow(CB),2]) But how do I defferentiate f()? g<-deriv(f(2:nrow(CB)),"x") Did not work out for me, or at least, I dont know how to get those "x", with g(x)=0. My ultimate goal, is to find all the local minima of CB[,2]. (mi...