similar to: ACF normalization.

Hi all, I am a freshman of R,but I am interested in it! Those days,I am learning pages on NIST,with url http://www.itl.nist.gov/div898/handbook/eda/section3/probplot.htm, I am meeting a problem about probability plot and I don't know how to plot a data set with R. Could somebody tell me the answer,and a example is the best! I will look forward to your answer. Thank you very much.

Hello all, Would somebody be kind enough to show me how to do a KS test in R for a lognormal distribution with ESTIMATED parameters. The R function ks.test()says "the parameters specified must be prespecified and not estimated from the data" Is there a way to correct this when one uses estimated data? Regards, Kwabena. -------------------------------------------- Kwabena Adusei-Poku

Hola a todos: Estoy intentado realizar un modelo senoidal de unos datos de radiación solar con el fin de afrontar el relleno de la serie y aplicar la prueba de Thom para verificar su homogeneidad [0]. De momento me encuentro con los siguientes problemas: 1- ¿Existe la prueba de Thom en R? ¿O debo crearme mi propia función? 2- Para la realización del modelo senoidal estoy siguiendo los pasos

Dear R-Users, How can I use chisq.test() as a goodness of fit test? Reading man-page I?ve some doubts that kind of test is available with this statement. Am I wrong? X2=sum((O-E)^2)/E) O=empirical frequencies E=expected freq. calculated with the model (such as normal distribution) See: http://www.itl.nist.gov/div898/handbook/eda/section3/eda35f.htm for X2 used as a goodness of fit test. Any

hi! I would like to ask help how to generate numbers from g-and-h distribution. This distribution is like normal distribution but span more of the kurtosis and skewness plane. Has R any package on how to generate them? Any help will be greatly appreciated. Thank you so much! Form, Filame Uyaco --------------------------------- [[alternative HTML version deleted]]

Does anyone have code to plot bihistograms in R? See http://www.itl.nist.gov/div898/handbook/eda/section3/bihistog.htm for a description of a bihistogram. -- Hal Varian voice: 510-643-4757 SIMS, 102 South Hall fax: 510-642-5814 University of California hal at sims.berkeley.edu Berkeley, CA 94720-4600 http://www.sims.berkeley.edu/~hal

I'd like to generate some plots like you'd see on the old "normal probability graph paper", like the first plot in: <http://www.itl.nist.gov/div898/handbook/eda/section3/normprpl.htm> except the horizontal scale would have 1%, 5%, 25%, 50%, 75%, 95%, 99%, or similar quantiles, with associated tick/grid lines. [still hunting around for a good example...] something like

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, from what you're writing: "The logaritmic transformation "shapiro.test(log10(y))" says: W=0.9773, p-value= 2.512e-05." it seems the log-values are not distributed normally and so original data are not distributed like a log-normal: the p-value is extremally small! Other tests for normality are available in package: nortest compare the log-transformation of your ecdf

I have run a t.test in R, and received these results: Two Sample t-test data: rsa and umple t = 0.9819, df = 10, p-value = 0.3493 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -76.1541 196.1541 sample estimates: mean of x mean of y 508.3333 448.3333 Can someone give me a detailed interpretation of the above results? Specifically,

R plotting experts: I have a bivariate dataset composed of 300 (x,y) continuous datapoints. 297 of these points are located within the y range of [0,10], while 2 are located at 20 and one at 55. No coding errors, real outliers. When plotting these data with a scatterplot, I obviously have a problem. If I plot the full dataset with ylim = c(0,55), then I cannot see the structure in the data in

NIST maintains a repository of Statistical Reference Datasets at http://www.itl.nist.gov/div898/strd/. I have been working through the datasets to compare R's results to their references with the hope that if all works well, this could become a validation package. All the linear regression datasets give results with some degree of accuracy except one. The NIST model includes 11 parameters,

Dear Users! I encountered with some problem in data reading while I challenged R (and me too) in a validation point of view. In this issue, I tried to utilize some reference datasets ( http://www.itl.nist.gov/div898/strd/index.html). And the result departed a bit from my expectations. This dataset dedicated to challenge cancellation and accumulation errors (case SmLs07), that's why this

I am guessing this is a very simple question, but this is only my second day with R so it is all still a bit imposing. I am trying to run an autocorrelation. I imported a CSV file, which has one column labeled "logistic". I ran the command: ACF(data$logistic,maxLag=10) However, I received the error: Error in UseMethod("ACF") : no applicable method for 'ACF'

Hi, I would like to use acf.plot on a correlogram that is computed externally. In other words, I would like to "fake out" the acf object. Is this possible?-- any help would be appreciated. TIA Martin

I need to modify the graph of the autocorrelation. I tried to do it through plot.acf but with no success. 1. I would like to get rid of the lag zero 2. I would like to have numbers on the x-axis only at lags 12, 24, 36, 48, 60, ... Could anybody help me in this? Any help will be appreciated Thank you for your attention Stefano [[alternative HTML version deleted]]

Hi I'm getting the following error that do not make sense to me, what am Idoing wrong ? > acf(Recsim[1,], lag.max=1) Error in acf(Recsim[1, ], lag.max = 1) : 'lag.max' must be at least 1 Regards EJ

Hello all, I found a funny problem with y-axis labels when plotting acf(matrix) - the labels are too close to one of the margins and cut in half. Here's the problem: test<-matrix(rnorm(200),ncol=4) acf(test) This doesn't fix the problem: test<-matrix(rnorm(200),ncol=4) par(mar=c(3,3,2,0.2),oma=c(0,0,0,0)) acf(test) This does fix the margin. I understand why, but not sure why ONLY

Hello, I was wondering if anybody could help me with this? I have plotted an acf function for a time series and am very happy with it. Now I am interested in calculating for myself the two values for the confidence intervals that are plotted on the graph of the acf. The confidence intervals do not appear to be returned from the acf function (is this true?). So far I haven't managed to

Hi, Im new to R so this question is quite fundamental. Im trying to compare some autocorrelations generated by the acf function to some theoretical correlations. How can I have acces to just the autocorrelations, for computation? This is some of my code: > acf.data<-c(acf(x)) > acf.data This is the R output: $acf , , 1 [,1] [1,] 1.000000000 [2,]