Displaying 2 results from an estimated 2 matches for "apprximate".
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approximate
2012 Feb 08
1
standard error for lda()
Hi, I am wondering if it is possible to get an estimate of standard error of the predicted posterior probability from LDA using lda() from MASS? Logistic regression using glm() would generate a standard error for predicted 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
2011 May 25
0
approximate function and find local peaks (Maxima or Minima)
...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]. (min()...