On Oct 2, 2014, at 2:29 PM, Jonathan Thayn <jth...@ilstu.edu> wrote:
> Hi Don. I would like to "de-rotate� the first component back to its original > state so that it aligns with the original time-series. My goal is to create a > �cleaned�, or a �model� time-series from which noise has been removed. Please cc the list with replies. It�s considered courtesy plus you�ll get more help that way than just from me. Your goal sounds almost metaphorical, at least to me. Your first axis �aligns� with the original time series already in that it captures the dominant variation across all four. Beyond that, there are many approaches to signal/noise relations within time-series analysis. I am not a good source of help on these, and you probably need a statistical consult (locally?), which is not the function of this list. > > > Jonathan Thayn > > > > On Oct 2, 2014, at 2:33 PM, Don McKenzie <d...@u.washington.edu> wrote: > >> >> On Oct 2, 2014, at 12:18 PM, Jonathan Thayn <jth...@ilstu.edu> wrote: >> >>> I have four time-series of similar data. I would like to combine these >>> into a single, clean time-series. I could simply find the mean of each time >>> period, but I think that using principal components analysis should extract >>> the most salient pattern and ignore some of the noise. I can compute >>> components using princomp >>> >>> >>> d1 <- c(113, 108, 105, 103, 109, 115, 115, 102, 102, 111, 122, 122, 110, >>> 110, 104, 121, 121, 120, 120, 137, 137, 138, 138, 136, 172, 172, 157, 165, >>> 173, 173, 174, 174, 119, 167, 167, 144, 170, 173, 173, 169, 155, 116, 101, >>> 114, 114, 107, 108, 108, 131, 131, 117, 113) >>> d2 <- c(138, 115, 127, 127, 119, 126, 126, 124, 124, 119, 119, 120, 120, >>> 115, 109, 137, 142, 142, 143, 145, 145, 163, 169, 169, 180, 180, 174, 181, >>> 181, 179, 173, 185, 185, 183, 183, 178, 182, 182, 181, 178, 171, 154, 145, >>> 147, 147, 124, 124, 120, 128, 141, 141, 138) >>> d3 <- c(138, 120, 129, 129, 120, 126, 126, 125, 125, 119, 119, 122, 122, >>> 115, 109, 141, 144, 144, 148, 149, 149, 163, 172, 172, 183, 183, 180, 181, >>> 181, 181, 173, 185, 185, 183, 183, 184, 182, 182, 181, 179, 172, 154, 149, >>> 156, 156, 125, 125, 115, 139, 140, 140, 138) >>> d4 <- c(134, 115, 120, 120, 117, 123, 123, 128, 128, 119, 119, 121, 121, >>> 114, 114, 142, 145, 145, 144, 145, 145, 167, 172, 172, 179, 179, 179, 182, >>> 182, 182, 182, 182, 184, 184, 182, 184, 183, 183, 181, 179, 172, 149, 149, >>> 149, 149, 124, 124, 119, 131, 135, 135, 134) >>> >>> >>> pca <- princomp(cbind(d1,d2,d3,d4)) >>> plot(pca$scores[,1]) >>> >>> This seems to have created the clean pattern I want, but I would like to >>> project the first component back into the original axes? Is there a simple >>> way to do that? >> >> Do you mean that you want to scale the scores on Axis 1 to the mean and >> range of your raw data? Or their mean and variance? >> >> See >> >> ?scale >>> >>> >>> >>> >>> Jonathan B. Thayn >>> >>> >>> ______________________________________________ >>> R-help@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. >> >> Don McKenzie >> Research Ecologist >> Pacific WIldland Fire Sciences Lab >> US Forest Service >> >> Affiliate Professor >> School of Environmental and Forest Sciences >> College of the Environment >> University of Washington >> d...@uw.edu > Don McKenzie Research Ecologist Pacific WIldland Fire Sciences Lab US Forest Service Affiliate Professor School of Environmental and Forest Sciences College of the Environment University of Washington d...@uw.edu [[alternative HTML version deleted]]
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