similar to: box priece Q statistic

I want to calculate the commulative sum of any numeric vector with the following command but this following command does not work "comsum" My question is , how we can calculate the commulative sum of any numeric vector with above command Thanks Zahid Khan Lecturer in Statistics Department of Mathematics Hazara University Mansehra. ---------------------------------

"contour" can't return a list of contours (it would be nice) "arrows" has different options from S-PLUS (no "open" or "rel" options, no "lwd" parameter or graphics parameter pass-through; "size" in S corresponds to "length" in R). [I know we needn't follow S-PLUS syntax slavishly, but it would be nice at least to

Dear R-Users My problem is quite simple: I need to use a fitted model to predict the next point (that is, just one single point in a curve). The data was divided in two parts: identification (x and y - class matrix) and validation (xt and yt - class matrix). I don't use all values in x and y but only the 10 nearest points (x[b,] and y[b,]) for each regression (b is a vector with the

Dear R users I get different F-statistic results for a "within" model, when using "time" or "twoways" effects in plm() [1] and when manually specifying the time control dummies [2]. [1] vignette("plm") [2] http://cran.r-project.org/doc/contrib/Farnsworth-EconometricsInR.pdf Two examples below: library("AER") data("Grunfeld", package =

Thank you for your reply. I am aware of the good reasons not to use the deviance estimate in binomial, Poisson, and gamma families. However, for the inverse Gaussian, the choice seems to me less clear cut. So I just wanted to compare two different options. I have used the dispersion parameter to compute the standardized deviance residuals: summary(model.gamma)$deviance.resid

Does anyone know which package include the computation of Cochran’s Q statistic in R? jlfmssm [[alternative HTML version deleted]]

Hi all, Can I run a cointegrating regression, for example delta Xt=a1(Yt-1-cXt-1)+E1t and delta Yt=-b1(Yt-1-cXt-1)+E2t with R were Xt and Yt are non stationary time series at t a,b,c are parameters and E1t and E2t are error terms at t. Yt-Xt is stationary Any suggestions are welcome. Best regards, /fb -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing

A question about: Kalman in R, time series and deJong-Penzer statistic - how to compute it using available artefacts of KalmanXXXXX? Background. in the paper http://www.lse.ac.uk/collections/statistics/documents/researchreport34.pdf 'Diagnosing Shocks in TIme Series', de Jong and Penzer construct a statistic (tau) which can be used to locate potential shocks. [p15, Theorem 6.1 and

On Sun, 27 Jul 2008, S Ellison wrote: > Looking at the legend() source the filled box line colour is hardcoded : > if (mfill) { > if (plot) { > fill <- rep(fill, length.out = n.leg) > rect2(left = xt, top = yt + ybox/2, dx = xbox, dy = ybox, > col = fill, density = density, angle = angle, > border =

Here are another three problems with logarithmic scales: 1) segments() does not work with logarithmic scales. I suggest to change lines 962-973 in "plot.c": for (i = 0; i < n; i++) { if (FINITE(xt(x0[i%nx0])) && FINITE(yt(y0[i%ny0])) && FINITE(xt(x1[i%nx1])) && FINITE(yt(y1[i%ny1]))) { GP->col = INTEGER(col)[i % ncol];

Hi all, I am modelling a time series with missing data. *Q1)* However, I am not sure if I should use the next *graphics* to understand my data: *a)* ACF & PACF (original series) *b)* ACF & PACF (residuals) * * *Q2)* I am using *tsdiag*, so I obtain a graphic with 3 plots: stand. residuals vs time; acf for residuals; Ljung-Box for residuals (it is wrong for residuals). I know that using

I fit a simple linear model y = bX to a data set today, and that produced 24 residuals (I have 24 data points, one for each year from 1984-2007). I would like to test the time-independence of the residuals of my model, and I was recommended by my supervisor to use the Ljung-Box test. The Box.test function in R takes 4 arguments:  x a numeric vector or univariate time series. lag the statistic

It looks just like the classical F-test for lack-of-fit, using estimate of `pure errors' from replicates, doesn't it? This should be in most applied regression books. The power (i.e., probability of finding lack-of-fit when it exists) of such tests will depend on the data. Andy > From: RenE J.V. Bertin > > Hello, > > I'd like to come back to this question I

Hi all, I want to estimate parameters from a VARMA(p,q)-Modell. The equations of the model or the model structures is given by: Xt=beta1+beta2*Xt-1+beta3*Yt-1+epsilon1 Yt=beta4+beta5*Yt-1+espilon2 epsilon1 and espilon2 are white noise. Xt is given by a vector of n elements e.g. (2, 4, 7, 9, …,n)’ and Yt is given by a vector of n elements e.g. (4,9,12,17,…,n)’. The lineVar from

Shouldn't the F statistic (and p value) for the x2 term in the following calls to anova() and add1() be the same? I think anova() gets it right and add1() does not. > d <- data.frame(y=1:10, x1=log(1:10), x2=replace(1/(1:10), 2:3, NA)) > anova(lm(y ~ x1 + x2, data=d)) Analysis of Variance Table Response: y Df Sum Sq Mean Sq F value Pr(>F) x1 1

Dear, i am a student and I need help in comparing between different slopes and finding whther there is a significant difference between them? Thanks a lot [[alternative HTML version deleted]]

I'm having trouble creating a looping variable and i can't see wher ethe problem arises from any hep gratfully appreciated First create a table x<-table(SURVEY$n_0,exposed) > x exposed False True Under 16 24 1 16-19 68 9 20-24 190 37 25-34 555 204 35-44 330 87 45-54 198 65 55-64 67 35 65+

Dermot MacSweeney pointed out to me that after my "fix" of legend(), points were no longer coming out placed in the middle of the lines, but at the right-hand edge. It turns out that naively swapping the order of point-drawing and line-drawing also messes up the bookkeeping that legend() does on the current x-location. Here's my patch, which fixes that bookkeeping (and incidentally

Perhaps I should have submitted this as a bug so that it would be officially tracked. It's not a big deal, but here it is again (I can't remember which version this patch is against, but I don't think legend() has changed since then ...) Basically, the problem is that if you want to have "opaque" points that overlay lines (rather than using type="b" and having

Hi, this is purely a time series question if anyone has a hint. I'm trying to complex demodulate a series at different frequencies. Say at $\lambda$ = 1/100, 1/41 and 1/23. (These correspond to various astronomical forcing frequencies of ice ages, incidentally). Now, following say Hasan (1982, Hand. of Stats) or Brillinger (1987, Fitting Cosines), I do the following: 1. from y, form the