Hello
I have a dataframe that looks like this:
Date Min Subj VAR1 VAR2 VAR3
1 8/30/2011 5min 1 34.41042 126.08490 55.3548387
2 8/30/2011 10min 1 34.53030 133.81343 61.6000000
3 8/30/2011 15min 1 34.66297 118.38193 11.8000000
4 8/30/2011 20min 1 34.82770 110.77767 6.6000000
5 8/30/2011 5min 2 36.36994 116.24861 41.2258065
6 8/30/2011 10min 2 36.37420 101.16457 13.6000000
7 8/30/2011 15min 2 36.37453 92.26340 0.4000000
8 8/30/2011 20min 2 36.37697 87.73650 0.0000000
9 8/30/2011 5min 3 35.25667 146.90037 10.0645161
10 8/30/2011 10min 3 35.36654 139.49364 6.0000000
11 8/30/2011 15min 3 35.33833 135.75633 0.4000000
12 8/30/2011 20min 3 36.01337 127.83797 0.0000000
13 8/30/2011 5min 4 35.26742 84.78603 0.9677419
14 8/30/2011 10min 4 35.17913 91.27093 1.8000000
15 8/30/2011 15min 4 35.09825 92.03692 13.4000000
16 8/30/2011 20min 4 35.36823 88.73337 4.8000000
and so on for more days.
I would like to check the correlation and p of variables VAR1 VAR2 VAR3.
if I use cor.test(tel$VAR1, tel$VAR2)
the observations are considered independent, and Indeed I got df=14
I have seen that I can obtain a correlation for each block using this
script:
http://stackoverflow.com/questions/2336056/how-to-do-correlation-with-blocks-or-repeated-measures
I was wandering what I should do for obtain a correlation that account for
all the blocks.
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