sliding window analysis with rollapply
Hi Li, I'm afraid I don't have much time to look at this right now -- I'm sort of swamped for the next 5 days -- so I'm forwarding back to the list so more eyeballs pass over it. In general, you should always cc the list so the archives remain complete and so that you get a quicker response.
On Thu, Oct 18, 2012 at 5:12 PM, Wang, Li <li.wang at ttu.edu> wrote:
Hi, Michael Thanks very much for your reply! My main concern is to calculate average Fst within a window. The window size, say 1000,000, means from position 1-1000,000. In this following case, no position within this window. But if the window is 1000,000 -- 2000,000, then the former 9 positions were within the window, and we can calculated the average Fst value. That is what I mean by rolling Fst again pos. I donot know if my intention could be achieved in R. pos Fst 264643 0.0845204537 264673 0.0060222786 264735 0.0865209872 265951 -0.0038305848 267707 0.0865209872 268666 0.0185608883 268741 0.0493999072 269643 0.0424427481 269771 0.1294509289 643220 -0.0160247705 643956 0.0106426936 644001 0.0411787797 644170 0.0302527151 644252 0.0067342624 644730 0.0073714424 645180 0.0331635458 648070 0.0919606246 648193 -0.0077790431 648477 0.0331635458 649139 0.0302527151 649536 0.0240243946 649537 0.0060054306 649810 0.0246680573 649860 0.0302527151 649861 0.0194870768 650399 0.0331635458 650427 0.0380601183 650602 0.0331635458 652468 0.0246680573 653322 0.039720035 653477 0.0246680573 653539 0.0380601183 653808 -0.0362652864 653839 0.0589780782 653863 0.0589780782 653979 0.1080478911 653998 0.1080478911 654016 0.0163679102 654104 0.0167725359 654156 0.1436247159 715529 -0.0750541068 715621 -0.1207825145 716629 -0.0696369342 719352 -0.0188278314 719359 -0.0188278314 719606 0.0355536571 720851 -0.0188278314 720907 0.0073714424 720948 -0.0661275377 721436 -0.1073576949 723537 0.0110375276 724027 0.0134550135 724119 -0.030982485 724725 0.1468391016 728718 -0.1216726698 728727 -0.043005885 730714 0.1111296269 731410 0.0239519768 732418 0.0676868746 Sorry for my stupid question. I am very willing to be educated. Best regards Li
________________________________________
From: R. Michael Weylandt [michael.weylandt at gmail.com]
Sent: Wednesday, October 17, 2012 7:51 AM
To: Wang, Li
Cc: r-help at r-project.org
Subject: Re: [R] sliding window analysis with rollapply
On Tue, Oct 16, 2012 at 11:17 PM, Wang, Li <li.wang at ttu.edu> wrote:
Dear List members
I want to do the sliding window analysis of some specific values. Here is my code:
require(zoo)
dat <- read.table("chr1.txt", header = TRUE, sep="\t")
dat2 <- cbind(dat[1,3]) #The first column is also important. It represents the position of the site on the chromosome.
TS <- zoo(c(dat2))
Two Style comments:
1) Perhaps you just want read.zoo()
2) No need to have the c() when you aren't combining things.
a <- rollapply(TS, width=1000000, by=200000, FUN=mean, align="left") #The third column values should roll against the first column values. I might be wrong here with the code.
3) rollmean() directly could also work here, but I'm not at all sure
what you mean by "the third column values should roll against the
first column values"
plot1 <- plot.default(a, col="red", type="l") #It returns me some errors for this command.
4) Generally better to call the generic plot() instead of the
specialized plot.default()
Michael
Error in plot.window(...) : need finite 'xlim' values
In addition: Warning messages:
1: In min(x) : no non-missing arguments to min; returning Inf
2: In max(x) : no non-missing arguments to max; returning -Inf
3: In min(x) : no non-missing arguments to min; returning Inf
4: In max(x) : no non-missing arguments to max; returning -Inf
Part of the original data looks like as follows:
pos Fit Fst Fis
12794 0.928380160665041 -0.0877263098996843 0.934156378600823
12816 0.901040947621283 0.0382417096943425 0.897106109324759
12821 0.901040947621283 0.0382417096943425 0.897106109324759
12827 0.901040947621283 0.0382417096943425 0.897106109324759
12855 0.909446752933768 0.00347893012209298 0.909130624726955
13324 0.498125727088629 0.0975914515920549 0.443850267379679
13338 0.53827858671811 0.103194947279406 0.485148514851485
13339 -0.059306330783135 -0.0198369843236668 -0.038701622971286
13379 0.610641507226514 0.175889055559966 0.527541169789892
13381 0.144593428517224 -0.0343016958072985 0.172962226640159
13390 0.216526396327467 0.179801071155318 0.0447761194029849
13454 0.498125727088629 0.0975914515920549 0.443850267379679
13457 0.309860615307135 -0.0501399771889025 0.342812006319115
13462 0.536517915763086 -0.0302077737766018 0.550108147080029
That is, Fst against pos.
I am very willing to be educated concerning the mistakes I made for the code.
All the best
Li
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