On 04/11/14 09:11, Jim Lemon wrote:
On Mon, 3 Nov 2014 12:45:03 PM CJ Davies wrote:
...
On 30/10/14 21:33, Jim Lemon wrote:
If I understand, you mean to calculate deviations for each individual
'chunk' of each transition & then aggregate the results? This is what
I'd been thinking about, but is there a sensible manner within R to
achieve this, or is it something for which it would be easier to
preprocess the data in an external tool? Is there some way to subset
the
data such that I can work over just contiguous 'chunks'?
Exactly. If there is some combination of existing variables that can be
combined to make a set of unique values for each "chunk", you can
calculate the deviations within each "chunk", then average the squared
deviations for each type of "chunk", weighting by the duration of the
"chunks" so that you don't bias the pooled variance toward the longer
"chunks".
Jim
I am stumped for a way of automating this process though. Each line of
log data looks like this;
2406 55.4 (-11.2, 1.0, -0.9) (-4.1, 1.0, 0.0) 7.077912 0.9203392 (0.0,
0.7, -0.1, 0.7) 8.129684 89.41537 -8.212769 (0.0, 0.7, -0.1, 0.7)
8.129684 89.41537 351.7872 1 0 0 False 0.15 3 37.76761 True False 0
transition 1
Where the last variable defines which transition is currently active.
However to separate these data into 'chunks' would involve making a
comparison between each line of data & the preceding line of data to
determine whether it is part of the same contiguous 'chunk'. Is this
something that would be better achieved using external preprocessing
written in a language I am more familiar with, as I haven't the foggiest
how I would approach this within R?
Regards,
CJ Davies
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