similar to: for/if loop in R

Hello This is my first post on this forum and I hope someone can help me out. I have a datafile (weeklyR) with returns of +- 100 companies. I acquired this computing the following code: library("tseries"); tickers = c("GSPC" , "BP" , "TOT" , "ENI.MI" , "VOW.BE" , "CS.PA" , "DAI.DE" , "ALV.DE" ,

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

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)

I have a question regarding the adf.test command in the tseries library. I have a vector of time series observations (2265 daily log prices for the OEX to be exact). I also have this same data in first-differenced form. I want to test both vectors individually for staionarity with an Augmented Dickey-Fuller test. I noticed when I use the adf.test command from the tseries library, the general

Hi everyone I am trying to do some time series analysis with daily temperature data (40 years). I have created a zoo object and ts object but can't apply stl function. It says the series is not periodic or has less than two periods. I've searched through google and found a lot of messages about this problem but not a solution/example to look for trend and seasonal component of a

Hi R-users, I'm trying to produce decompositions of a multiple time-series, grouped by a factor (called "area"). I'm modifying the code in the STLperArea function of package ndvits, as this function only plots produces stl plots, it does not return the underlying data. I want to extract the trend component of each decomposition ("x$time.series[,trend]), assign a name

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

I'm trying to simulate trend data over a five year period. I want different trend profiles...the simplest being a linear trend. I've been using the following code: patBdta1 <- NULL for(i in 1:100) patBdta1 <- rbind(patBdta1,c(yr1= mean(rbinom(50,1,.50)), yr2 =mean(rbinom(50,1,.51)), yr3 =mean(rbinom(50,1,.52)),

Dear list users How is it possible to visualise both a linear trend line and a quadratic trend line on a plot of two variables? Here my almost working exsample. data(Duncan) attach(Duncan) plot(prestige ~ income) abline(lm(prestige ~ income), col=2, lwd=2) Now I would like to add yet another trend line, but this time a quadratic one. So I have two trend lines. One linear trend line

First, thanks to everyone who helped me get to grips with R in (x)emacs (I get confused easily). Special thanks to Stephen Eglen for continued support. My question is about non-linear binning, or density functions over distributions governed by a power law ... y ~ mu*x**lambda # In one of its forms # (can't find Pareto in the online help) Looking at the following

Hello all, I have two questions about anova (one is probably VERY basic...) 1 - when one asks for a summary of a trend surface created with surf.ls, he/she gets: > summary(g3r) Analysis of Variance Table Model: surf.ls(np = 3, x = gradiente$east, y = gradiente$north, z = gradiente$num1) Sum Sq Df Mean Sq F value Pr(>F) Regression 215.7182 9 23.968693976

Hi, I am trying to run many different regressions using a FOR Loop. The input data that is read into R has the following variables · Volume · Price1 · Price2 · Price3 · Price4 · Price5 · Trend · Seasonality I want to run 5 regressions, with the Volume as an dependent variable and Price, Trend & Seasonality as

Out of curiosity, I played a bit with the new function packageDate() from April, 4th. All works fine, except using it without any argument. In this case, it segfaults: #> packageDate() Error in packageDescription(pkg, lib.loc = lib.loc, fields = date.fields) : argument "pkg" is missing, with no default *** caught segfault *** address 0x77bc3c0, cause 'memory not mapped'

(My apologies if this is a repeated posting. I couldn't find any trace of my previous attempt in the archive.) I'm having trouble with forecast() in the dse2 package. It works fine for me on a model without a trend, but gives me NaN output for the forecast values when using a model with a trend. An example: # Set inputs and outputs for the ARMA model fit and test periods

Hi mailinglist members, I’m actually working on a time series prediction and my current approach is to decompose the series first into a trend, a seasonal component and a remainder. Therefore I’m using the stl() function. But I’m wondering how to get the single components in order to predict the particular fitted series’. This code snippet illustrates my problem: series <-

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

Hi everybody, I'm trying to construct contrasts for an ANOVA to determine if there is a significant trend in the means of my groups. In the following example, based on the type of 2x3 ANOVA I'm trying to perform, does the linear polynomial contrast generated by contr.poly allow me to test for a linear trend across groups? doi=data.frame( Group=c( rep(1, 5), rep(2, 5), rep(3, 5),

G'day, I am looking at monthly reports, and have three series of monthly data from 2007 to 2009. I would like to show the season decomposition of these two series side by side on the one device, however using plot doesn't seem to respect any use of layout(matrix(1:3, ncol=3)) or par(mfcol=c(1,3)). I'm guessing that this means that the plot(stl) perhaps uses them, but I can't find

I need to build up a few servers and routers, and am wondering how FreeBSD 9.1 is shaping up. Will it be likely to be more stable and robust than 9.0-RELEASE? Are there issues that will have to wait until 9.2-RELEASE to be fixed? Opinions welcome. --Brett Glass

I got a set of data which has seasonal trend in form of sinx, cosx, I don't have any idea on how to deal with it. Can you give me a starting point? Thanks, Terry