On Thu, 11 Dec 2008, tsunhin wong wrote:
Dear all R users,
I am going to use R to process some of my physiological data about eye.
The problem is the recording machine does not sample in a reliably
constant rate: the time intervals between data sampled can vary from
9msec to ~120msec, while most around in the 15-30msec range.
The below is a fraction of a single data file of a trial:
Time CursorX CursorY Pupilsize
1811543 -1 -1 -1
1811563 -1 -1 -1
1811584 511 370 4.175665
1811603 511 368 4.181973
1811624 521 368 4.210732
1811644 512 377 4.149632
1811664 524 377 4.275845
1811684 518 368 4.236212
1811703 516 370 4.238384
1811725 507 364 4.181157
1811744 509 371 4.185016
1811764 509 377 4.231987
1811784 514 387 4.252449
1811802 515 388 4.273726
My goal is to "resample" these data so that the "Time" column
increments by a regular interval, and the other columns of data are
the averages (or estimates) at the point in time according to
available data points.
'resample' is the probably wrong word in a forum in which here are many
statisticians for whom that word has special meaning.
'interpolate' is more appropriate.
Following the _posting guide_, when I do
help.search("interpolate")
I get a number of useful hits. Like
stats::approx Interpolation Functions
Read the help page
?approx
and try
example( approx )
Of course, this is R and there are many other ways to skin this cat like
predict( gam( <...> ), newdata = interpolation.points )
from the mgcv package.
HTH,
Chuck
I have done something that I use a regular interval that is larger
than the naturally occurring record machine, i.e. > 120msec for
example, and acquire an average of the available data points for any
particular regular time interval.
Now, I need to achieve resampling for smaller regular interval: i.e.
5msec intervals, and interpolate / intrapolate the missing data points
from the available ones.
i.e. I may have to split up data points into the number of the regular
intervals that it may occupied in time.
Do you know if there is any package that is doing something similar?
And because of the size of the data and computational demand (1500
files each with 2000-8000+ lines), can you suggest me some
(algorithmically) more efficient way of doing this?
Thanks a lot!
Regards,
John
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
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Charles C. Berry (858) 534-2098
Dept of Family/Preventive Medicine
E mailto:cbe...@tajo.ucsd.edu UC San Diego
http://famprevmed.ucsd.edu/faculty/cberry/ La Jolla, San Diego 92093-0901
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