similar to: range.bars in stl

Hi All, How do I can create a new vector, i.e. 'seasonal' or 'trend' from the resultant seasonal or trend component of the Time.Series object produced by STL, and how I could superpose in the same graphic, i.e. original data and trend or seasonality? Thanks in advance! Antonio Antonio Rodr?guez Verdugo CICEM Agua del Pino Huelva Oceanography and Coastal Resources, PhD Program,

Dear helpeRs, Is it possible to change the shading colour of the range bars in the plot generated by plot.stl? By default they are grey, but I would prefer them white (I am preparing some graphics for a powerpoint presentation so I'm inverting all colours). As far as I can see plot.stl allows you to turn off the range bars, but nothing about the shading colour. I tried to look at the

Hi, I have a monthly time series with missing values and I would use stl function to identify seasonality. I tried all settings of na.action but the result is the same: stl(tm245,s.window=11, na.action=na.pass) Error in stl(tm245, s.window = 11, na.action = na.pass) : NA/NaN/Inf in foreign function call (arg 1) Can you help me? Thanks Andrea Toreti [[alternative HTML version

I am running R 1.2.2 under Linux. When using the function stl in the ts library, how can I save the seasonal component? What I would like was something like: library(ts) data(nottem) data.stl <- stl(nottem, "per") x <- data.stl$sea This what I get: > x NULL I would, however, like to store in x the seasonal component. Thanks in advance. Francisco. -- Francisco

Greetings: I'm using the time series decomposition routine "stl" from the package "stats". But how do I get the results into a vector to work with them? example: data(AirPassengers) m<-stl(AirPassengers,"per") print(m) This lists the output but can't figure out how to extract the individual series like seasonal, trend, irregular. Thanks, Bob

Hi, I try to figure out what the stl-function exactly do. I was reading the paper by Cleveland et al. (1990) and tested some features of stl (the ability to decompose time series with missing values and the robustness feature). I tried the following: > data(co2) > co2.na <- co2 > is.na(co2.na[c(50, 100)]) <- TRUE > plot(stl(co2.na, s.window = 12, na.action = na.exclude)) With

Hi there, My name is Renan X. Cortes, student of Statistics, from south of Brazil, and I'd like to ask you a few questions about decomposition of time series. In R, when I fit the decomposition using the "stl" function, an object is returned when ask the summary of the fit, called STL.seasonal (%), STL.trend (%) and STL.remainder (%). Once the decomposition is additive,

I need to investigate how rollmean and the trend returned from stl differ. I am trying to find out exactly what the trend part of stl is (I have just started coding in R and do not know fortran). I need to extract this because it will be used in further calculations, and it needs to be verified to make sure that I am using the right process. I would like to use this to remove the seasonal

Hi all, I'm working on decomposition and comparison of several time series. I'm interested in extracting the trend components for each time series using the stl function and overlaying them on one another. I'm not sure how to plot the trend function alone and to do the overlay using some kind of loop. If anyone has any insight that would be great! thanks, Katie -- View this

Hi all, How can the label of the x-axes in the plot() of a stl.object be adapted? e.g., When plotting: plot(stl(nottem, "per")) In the labels of the x-axes is “time”. How can this be changed to e.g., “Time (dekade) “? It does not work with xlab or others anymore… Thanks, Jan _______________________________________________________________________ Ir. Jan Verbesselt Research

Hi, I'm astudent in hydrobiology and I actually work on river's discharge and try to extract from my data the seasonal and trend components. I use STL function but I have several problems in understanding what this function have done. I'd like to know what means the IQR results which gave me some % about the seasonal component, trend component and the remainder component.

Hi everyone. I'm having some troubles with STL function to decompose some data. My issue is that I have monthly data from September 2005 up to August 2010 i. e. 60 observations. I define it in the following way: *u<-read.csv("C:/CELEBREX.csv",header = TRUE) u.ts<-ts(u, start=c(2005,9), frequency=12) * The issue is that when I try to use stl(u.ts, 'per') Then the

Hi, This is actually a theory question. I'm a bit confused by the s.window parameter in the stl() function (which is in the ts package). For example, in the stl documentation it uses the nottem data, and then: plot(stl(nottem, s.win = 4, t.win = 50, t.jump = 1)) What does it mean by s.win = 4? Is it because a year has 4 seasons (namely Spring, Summer, Autumn and Winter)? If so will it

Hi, I have imported a csv file into R which contains one column (the rate er 100,000 population of a disease, by month over 11 years) I coerced into a time series using the following function, tstkr<-ts(tkr,deltat=1/12) This seems to work fine, and when I check for the class of the object using class(tstkr) I get "ts" as the response. When I try to use the stl function in

Mainline and 4.3 branch GCC libstdc++ have removed string.h access from STL map etc. As a result several LLVM System files and others do not compile on mainline and 4.3. Aaron -------------- next part -------------- An HTML attachment was scrubbed... URL: <http://lists.llvm.org/pipermail/llvm-dev/attachments/20070518/5ad77408/attachment.html>

Hello, anyone got an idea on how to use stl() so that the remainder eventually becomes white noise? i used stl repeatedly but there is autocorrelation in the remainder that i can't get rid of. os: linux suse9.3 ------------------------------------------------ Sebastian Leuzinger Institute of Botany, University of Basel Sch??nbeinstr. 6 CH-4056 Basel ph 0041 (0) 61 2673511 fax 0041 (0)

Currently I find that if I call stl() repeatedly I can use the weights array that is part of the stil output to detect outliers. I also find that if I repeatedly call stl() (replacing the outliers after each call) that the "remainder" portion of the stil output gets reduced. I am calling it like: for(.index in 1:4) { st <- stl(mt, s.window=frequency(mt), robust=TRUE)

Hi all, I have a time series that contains double seasonal components (48 and 336) and I would like to decompose the series into the following time series components (trend, seasonal component 1, seasonal component 2 and irregular component). As far as I know, the STL procedure for decomposing a series in R only allows one seasonal component, so I have tried decomposing the series twice. First,

I am trying to understand the nuances of STL (seasonal trend decomposition with loess) based on William Cleveland's (and others?) original development. I do not understand the specification or use of "frequency components" or equivalent "low-pass filter" components in the stl() function. I have run the stl() function on a standard example data (co2) in both S-Plus and

Dear R Helpers, Currently I'm working with the ts package of R and created a TimeSerie from pixels extracted from satellite imagery(S10 NDVI data, 10 daily composites). I'm trying to decompose this signal in different signals (seasonal and trend). When testing out the STL method is says => Only univariate timeseries are allowed, but the current Timeserie I'm using is univariate!