similar to: how to test for stationarity in time series?

dear Researchers, i am looking for a function to plot a barplot for each mean value and the related standard deviation, and i can close my week. This is an example of my data set. really Thanks in advance for any help or suggestions Gianni My.mean <- data.frame(Mean=c(0.4108926,0.3949009,0.4520346, 0.4091665,0.4664066,0.3048296,0.4297226,0.4056383,

Hi does anyone know how to do a logistic regression in R? any help appreciated Carl -- View this message in context: http://r.789695.n4.nabble.com/Logistic-regression-tp4512658p4512658.html Sent from the R help mailing list archive at Nabble.com.

Buen día a todos. Tengo un problema.Podría ayudarme diciendóme cómo se llama la paquetería para encontrar el comando "bs.data", lo he estado buscando para ver como escribir el comando en R y no me aparece una paquetería para ver la descripción. Saludos. [[alternative HTML version deleted]]

I am looking for R packages that can make a Logistic Regression model with parameter estimation by Maximum Likelihood Estimation. Many thanks for helping out.

Hey there, I was wondering if someone could tell me if there's a package or command that allows me to compute a GINI coefficient using a vector of weights. Also the coefficient should be bias corrected. Diego Rojas [[alternative HTML version deleted]]

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

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

Dear R list members, Please excuse my ignorance but as a new comer to R I was wondering if anyone knows of any functions in R or Splus that can test a time-series for non-stationarity such as the Pettitt or the Mann-Kendall tests. Kind regards, Jon Nott [[alternative HTML version deleted]]

Hi every one, Thanks for every one who are all supporting to us. we want some clarification on output in R. I have generated summary statistics output for dataset (E.g. sales) in output window. Now i want to store that output in a html or pdf in a table format. if possible can any one provide code for this one. Thanks in advance. -- View this message in context:

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, 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

Hola Carlos. Muchas gracias. No es exactamente lo que estoy buscando (sería genial ver que alguien tiene un paquete con la prueba de Nam-D'Agostino) pero puedo aprovechar algo de código. Encontré alguna referencia al test de Gronnesby&Borgan en el paquete stcoxgof de Stata, pero estoy torpe para encontrar algo hecho en R (y me extraña que no haya nada) Un saludo, Miguel. De: Carlos

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 there, Does anyone know if R has a function for testing whether a time series is stationary?? Thanks in advance, Wayne Dr Wayne R. Jones Statistician / Research Analyst KSS Group plc St James's Buildings 79 Oxford Street Manchester M1 6SS Tel: +44(0)161 609 4084 Mob: +44(0)7810 523 713 KSS Ltd A division of Knowledge Support Systems Group plc Seventh Floor St James's

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

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

By any chance are there any alternatives to image(...) and filled.contour(...) I used Rseek to search for that very topic, but didn't turn over any leads... http://www.rseek.org/?cx=010923144343702598753%3Aboaz1reyxd4&newwindow=1&q=alternative+to+image+and+filled.contour&sa=Search&cof=FORID%3A11&siteurl=www.rseek.org%252F#1238 I'm sure there are some out there, but

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 <-

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"