Hi,

I have a data-matrix:

> CB
                         Zeit                Low
2   2011-05-02 08:05:05 7596.0
3   2011-05-02 08:10:06 7593.5
4   2011-05-02 08:15:11 7594.5
5   2011-05-02 08:20:15 7597.5
6   2011-05-02 08:25:18 7595.0
7   2011-05-02 08:30:20 7593.5
8   2011-05-02 08:35:21 7593.0
9   2011-05-02 08:40:21 7593.0
10  2011-05-02 08:45:25 7599.0
11  2011-05-02 08:50:34 7596.0
12  2011-05-02 08:55:59 7591.0
13  2011-05-02 09:01:00 7590.5
14  2011-05-02 09:06:00 7590.5
15  2011-05-02 09:11:04 7590.5
16  2011-05-02 09:16:04 7591.0
17  2011-05-02 09:21:06 7593.0
18  2011-05-02 09:26:08 7596.0
19  2011-05-02 09:31:09 7596.0
20  2011-05-02 09:36:10 7599.0
21  2011-05-02 09:41:11 7601.5
22  2011-05-02 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() gives 
only the global minimum)

Any suggestions how to do it?

Thanks for your help in advance.
Michael


        
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