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
[[alternative HTML version deleted]]
______________________________________________ R-help at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.