similar to: How are the coefficients for the ur.ers, type DF-GLS calculated?

Hi all, as I am trying to move slowly from just "working" to "good" code, I'd like to ask if there's a smarter way than using a for-loop in tasks like the example below. I need to obtain the extrema of the cumulated sum of a detrended time series. The following code is currently used, please have a look at the comments for my questions and remarks: system.time({ X

Hi All, I'm new to R and am trying to run a unit root test on the vector "y" (a time series of inflation (i.e. changes in the Consumer Price Index quarter on quarter)). I've run the Augmented-Dickey-Fuller Test below (R's URCA package). It gives me an error that it cannot find the function ur.df unless I comment out the third last line of code (see below). I try to call

Dear R-users, Has anyone written a function for multifractal detrended fluctuation analysis? The "fractal" package does mono-fractal DFA, but not multifractal as far as I can tell. The MF-DFA approach is presented in: J. W. Kantelhardt, S. Zschiegner, E. Koscielny-Bunde, S. Havlin, A. Bunde, and H. E. Stanley, "Multifractal Detrended Fluctuation Analysis of

I am trying to use the "kernel" function. To understand how it works I tried out some of the examples. None of them works as shown in the following: > kernel("daniell", 50) # Error in kernel("daniell", 50) : unused argument(s) (50) > kernel("daniell", 10) # Error in kernel("daniell", 10) : unused argument(s) (10) >

Hi, I hope someone knows the answer to this or has a real good reference about it (I am using Legendre & Legendre, Numerical Ecology, 1998).... My data is a data.frame with locations as rows and vegetation assemblages / species as columns. I've done a PCA, a correspondance analysis (CA) using ca in ca package and a detrended correspondance analysis (DCA) using decorana from vegan package.

Hello, I have what should be an easy question. I'm a new r user and making the transition from menus to the command line so as to do batch processing of tons of data. One of my data streams needs to be detrended. It's a vector of numbers that follows a negative exponential decay. I need to fit a curve to it and use the residuals as an object. The data looks something like this: foo.dat

Dear members, I am performing multivariate analysis on marine benthic populations using R. At first glance I found ca and VEGANO packages to be the suitable for the task, but neither has incorporated Detrended Canonical Correspondence Analysis (DCCA), which is just the method I want to apply on my data. I've looked for alternative packages containing the method, but my suspicion is that

Hi All, I have a question about TableGen RegisterClass definition. I need to map different size of MVTs into a register class as below. def TestReg : RegisterClass<"Test", [v8i32, v4i32], ...> When I look at TableGen and CodeGen, it looks the types are used as following: 1. MCRegisterClass's RegSize and Alignment 2. SpillSize in TableGen 3. Type constraint for instruction

Dear all, I'm performing a detrended correspondence analysis on vascular plant community data (296 species), and I have a question on the species scores projected in the ordination diagram. When I run a ordiplot all species are projected in the output graph, but I'd like to restrict the number of species plotted in the final graph. Some species are so rare in the data, that no relevant

Good day, I'm using a VAR model to forecast sales with some extra variables (google trends data). I have divided my dataset into a trainingset (weekly sales + vars in 2006 and 2007) and a holdout set (2008). It is unclear to me how I should predict the out-of-sample data, because using the predict() function in the vars package seems to estimate my google trends vars as well. However, I want

Hi I'm trying to utilize the break command for breaking the loop when the p-value is less than 10 per cent using the urca package. But it does not break the loop, anyone that can help me? library(urca) set.seed(1) a1 <- runif(100) lag.max <- function(object, n = 12){ matris <- matrix(NA, nrow = n) for(i in 1:n) { matris[i] <- ur.df(object, lags = i,

Dear all, in my TestRegisterInfo.td file, I defined a register like this: class TestReg<bits<6> enc, string name> : Register<name> { let HWEncoding{5-0} = enc; let Namespace = "TEST"; } def D0 : TestReg<0x01, "d0">, DwarfRegNum<[1]>; but when I compile, the result I have in TestGenAsmMatcher.inc is this: case 'd': // 7

Hi I'd like to know if the R can do the DCCA? Because I can't find the package about the DCCA in R. If it can not, please introduce a free software to me, which you think can easily do the work , thanks! jeff

On 9/17/2015 7:04 AM, Sky Flyer via llvm-dev wrote: > It seems like d0 is always 14! > I check it with ARMGenAsmMatcher.inc it was the same! > How is it possible? because it should give the same register value that > matches the underlying platform not any autogenerated value!? The returned number is the register id as defined in <YourTarget>GenRegisterInfo.inc. These numbers

Hello, I am using the ur.df function from the {arca} package to run the augmented Dickey-Fuller unit root test on several time series. However; I do not understand the econometric interpretation of the the "phi1" and "phi2" test-statisitc which are output if you choose a "trend" or "drift" model. I looked at the source code for the function but I do not

Dear R users, I am applying the augmented-Dickey-Fuller Unit Root Test (ur.df function of the urca package) to a time series of approximately 50 values. To be sure I understood what was going on with the ur.df function, I checked the critical values of the 3 test statistics (tau, phi2 and phi3 if a trend is included) or the 2 test statistics (tau and phi1 if only a drift is included) with the

Hi R-users, I am using arima to fit a time series. Now I would like to include an intervention component "It (0 before intervention, 1 after)" using different types of impacts, that is, not only trying the simple abrupt permanent impact (yt = w It ) with the xreg option but also trying with a gradual permanent impact (yt= d * yt-1 + w * It ), following the filosophy of Box and Tiao

I'm working with the DFA (detrended fluctuation analysis) function in the package fractal on postural sway data, and for the life of me can't get the results to turn out as they should given what my data looks like. The data resemble a random walk, but the Hurst exponent estimates that I'm getting at ~0.9 instead of ~1.4, which is the value a researcher I've been working with

I was wondering if anyone could help with choosing optimal lag length for ZA test. There have been two lag order selection methods commonly used in the literature: 1) The ZA paper recommends to run the test with maximum number of lags. Then the lag order is reduced sequentially until the longest lag is statistically significant; 2) One could also use AIC or SBC or other criteria to choose lag

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];