similar to: Detecting Growth Trends

Hi all, I was wondering if there is a way to output text tables with the color of the text corresponding to a condition. More specifically, I"m outputting an time series table and want the console colors to be green>0 and red<0. This is very easy to do in excel using conditional formatting. Any ideas on how to do it here? thanks. P.S. I've thought about using a heatmap, but it

Hi, Am I interpreting the results properly? Are my conclusions correct? > KPSS.test(df) ---- ---- KPSS test ---- ---- Null hypotheses: Level stationarity and stationarity around a linear trend. Alternative hypothesis: Unit root. ---- Statistic for the null hypothesis of level stationarity: 1.089 Critical values: 0.10 0.05 0.025 0.01 0.347 0.463

I was wondering if anyone has a way out of the limitation that you must use equal length x,y, and z sequence lengths. For instance, x<-seq(1,100) y<-seq(1,100) z<-rnorm(100) scatterplot3d(z,x,y) works fine. However, if I get some results that has a different y subset length, such as x<-seq(1,100) y<-seq(1,300) z<-rnorm(100) scatterplot3d(z,x,y) I get the following error:

Does anyone know of a test for stationarity of a time series, or like all ordination techniques it is a qualitative assessment of a quantitative result. Books, papers, etc. suggestions welcome. thanks Stephen -- Let's not spend our time and resources thinking about things that are so little or so large that all they really do for us is puff us up and make us feel like gods. We are

Hi, How can I access the Values stored in kpssstat-class given by KPSS.test function and store it in a variable. For example: >x <- rnorm(1000) >test <- KPSS.test(ts(x)) >test ---- ---- KPSS test ---- ---- Null hypotheses: Level stationarity and stationarity around a linear trend. Alternative hypothesis: Unit root. ---- Statistic for the null

Hi all, Can anyone clear my doubts about what conclusions to take with the following what puts of some time series tests: > adf.test(melbmax) Augmented Dickey-Fuller Test data: melbmax Dickey-Fuller = -5.4075, Lag order = 15, p-value = 0.01 alternative hypothesis: stationary Warning message: p-value smaller than printed p-value in: adf.test(melbmax)

Hi All, First of all, could you tell me what the "KPSS Level" in the output of the test means? I have a series, x, of periodic data and tried kpss.test() on it to verify its stationarity. The tests gave me the p-value above 0.1. Since the null hypothesis N0 is that the series _is_ stationary, this means that I cannot reject N0. But the series does look periodic! So does all this

This is not exactly an R question but the R code below may make my question more understandable. If one plots sin(x) where x runs from -pi to pi , then the curve hovers around zero obviously. so , in a"stationary in the mean" sense, the series is stationary. But, clearly if one plots the acf, the autocorrelations at lower lags are quite high and, in the "box jenkins"

is that 'x' is level or trend stationary. I did this > s<-rnorm(1000) > kpss.test(s) KPSS Test for Level Stationarity data: s KPSS Level = 0.0429, Truncation lag parameter = 7, p-value = 0.1 Warning message: p-value greater than printed p-value in: kpss.test(s) My question is whether p=0.1 is a good number to reject N0? On the other hand, I have a

How can I make sure the residual signal, after subtracting the trend extracted through some technique, is actually trend-free ? I would greatly appreciate any suggestion about some Stationarity tests. I'd like to make sure I have got the difference between ACF and PACF right. In the following I am citing some definitions. I would appreciate your thoughts. ACF(k) estimates the correlation

Hi all, Could anybody tell me how to test for stationarity in time series? Thanks a lot! [[alternative HTML version deleted]]

Hello, I am very new to R and Time Series. I need some help including R codes about the following issues. I' ll really appreciate any number of answers... # I have a time series data composed of 24 values: myinput = c(n1,n2...,n24); # In order to make a forecasting a, I use the following codes result1 = arima(ts(myinput),order = c(p,d,q),seasonal = list(order=c(P,D,Q))) result2 =

Hi all see $subj http://www.google.com/trends?q=llvm congrats! P.S. sorry if known. best regards -- Valery A.Khamenya -------------- next part -------------- An HTML attachment was scrubbed... URL: <http://lists.llvm.org/pipermail/llvm-dev/attachments/20080318/2bfc6887/attachment.html>

I'm trying to understand how to use kpss.test() properly. If I have a level stationary series like rnorm() in the help page, shouldn't I get a small p-value with the null hypothesis set to "Trend"? The (condensed) output from kpss.test() for the two possible null hypotheses is given below. I don't see any significant difference between these results. > x <-

Can someone explain to me in what way the new (dpqr)gamma parameter can be interpreted as a rate (when shape != 1)? The only gamma rate that I am aware of is the hazard rate given by dgamma/(1-pgamma), the log of which is returned by my hgamma function (event library). Jim -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

Hi, Out of curiosity, has anyone managed to use mail-trends[0] to analyse their e-mails. I thought about trying it out, but got stuck midstream. The mail-trends scripts work very well with gmail, but because they say it is supposed to work with _any_ IMAP server, I thought I could get it running with Dovecot too. I know this is NOT a mail-trends support group though, but I believe there is

Hi I am thinking of ripping albums to a single flac file with embedded cuesheet. As track and index points have to be on a 588 sample boundary due to the CD TOC standard working in 588 sample frames, I thought it may be beneficial to rip CDs with a blocksize of 588 samples. According to the format page on sourcefourge a stream is subset if "The blocksize bits in the frame header must be

Dear List: the CODA and BOA packages for the analysis of MCMC output yield different results on two dignostic test of convergence: 1) Geweke's convergence diagnostic; 2) Heidelberger and Welch's convergence diagnostic. Does that imply that the CODA and BOA packages implement different ``flavors'' of the same test? I paste below an example. Geweke's test

Dear R-list, I have a statistical problem with the comparison of short time-series, representing densities of fish in different streams. For each stream (6 in total, here below showed only part of the dataset) I have 8 years of density data, as follows: year density stream 1 2000 0.51 stream1 2 2001 0.87 stream1 3 2002 0.68 stream1 4 2003 0.56 stream1 5 2004 0.50 stream1 6

Hi R users! I've come across using kpss tests for time series analysis and i have a question that troubles me since i don't have much experience with time series and the mathematical part underlining it. x<-c(253, 252, 275, 275, 272, 254, 272, 252, 249, 300, 244, 258, 255, 285, 301, 278, 279, 304, 275, 276, 313, 292, 302, 322, 281, 298, 305, 295, 286, 327, 286, 270, 289, 293, 287,