similar to: dw statistic

Hello everybody. I would like to pose a question regarding a warning message after running a function of my own. The function I created for computing the p-value of the dw statistic has a command line like this: »dwf0 <- function(dw,eigen) { carfun <- function(x) { (prod(1+2*(eigen-dw)*1i*x)^(-1/2)-prod(1-2*(eigen-dw)*1i*x)^(-1/2))/(1i*x) } ; 1/2+integrate(f=carfun,lower = 0,upper =

Hi, P-values in Durbin-Watson test obtained through the use of functions available in packages "lmtest" and "car" are different. The difference is quite significant. function "dwtest" in "lmtest" is much faster than "burbinwatson" in "car". Actually, you can take a nap while the latter trying to calculated Durbin-Watson test. My question

Should "dwtest" and "durbin.watson" be giving me the same DW statistic and p-value for these two fits? library(lmtest) library(car) X <- c(4.8509E-1,8.2667E-2,6.4010E-2,5.1188E-2,3.4492E-2,2.1660E-2, 3.2242E-3,1.8285E-3) Y <- c(2720,1150,1010,790,482,358,78,35) W <- 1/Y^2 fit <- lm(Y ~ X - 1) dwtest(fit,alternative="two.sided")

Hi all, I am doing a two-sided DW test: H0: rho = 0 H1: rho =/= 0 My understanding is that most test statistics tables are one-sided. It's the way they created the table. So from online, by doing Googling, I found a bunch of DW tables for Confidence Level 5%. Those tables can answer my two-sided question at 5x2 = 10% confidence level. But what if I want two-sided test at 1% and 5%

Hello! I have a data frame mysample (sorry for a long way of creating it below - but I need it in this form, and it works). I regress Y onto X1 through X11 - first without weights, then with weights: regtest1<-lm(Y~., data=mysample[-13])) regtest2<-lm(Y~., data=mysample[-13]),weights=mysample$weight) summary(regtest1) summary(regtest2) Then I calculate Durbin-Watson for both regressions

Sir,   I am unable to get the source code for Durbin-Watson test, as I want to calculate the p-value for Durbin Watson statistic using interpolation method. I sent this mail to r-help, but it was rejected, please suggest me some way. I will be highly greatful to you. Thanks in advance Ramesh [[alternative HTML version deleted]]

Hi, I'm dealing with time series. I usually use stl() to estimate trend, stagionality and residuals. I test for normality of residuals using shapiro.test(), but I can't test for autocorrelation and heteroskedasticity. Is there a way to perform Durbin-Watson test and Breusch-Pagan test (or other simalar tests) for time series? I find dwtest() and bptest() in the package lmtest, but it

Hi, I ran an experiment with 3 factors, 2 levels and 200 replications and as I want to test for residuals independence, I used Durbin-Watson in R. I found two functions (durbin.watson and dwtest) and while both are giving the same rho, the p-values are greatly differ: > durbin.watson(mod1) lag Autocorrelation D-W Statistic p-value 1 -0.04431012 2.088610 0.012 Alternative

Dear R list, I have a sequence of weekly observations of number of adults and larvae in various size classes from a butterfly population living in a subtropical area with pronounced wet and dry seasons. Wet and dry seasons are each defined 26 weeks long with fixed start and end dates. The data span 103 weeks (two seasons each of wet and dry) with some missing weeks. What I would like to do is

Dear editor: I'm writing this letter to enquire where can I download the package of "lmtest". Can you send me this package? THanks a lot. Best regards, Shu-Fei Wu

I have been reading about autocorrelation in linear models over the last couple of days, and I have to say the more I read, the more confused I get. Beyond confusion lies enlightenment, so I'm tempted to ask R-Help for guidance. Most authors are mainly worried about autocorrelation in the residuals, but some authors are also worried about autocorrelation within Y and within X vectors

Dear R People: Suppose we have a regression model that we will call y.lm We run the Durbin Watson test for autocorrelation and we find that there is positive autocorrelation, and phi = 0.72, say. What is our next step, please? Do we calculate the following yprime_t = y_t - 0.72y_t-1, x1prime_t = x1_t - 0.72x1_t-1, and so on, and re-fit the linear mode? I haven't done this in a while.

I have a question about a problem I came across while I was adding support for aggregate va_arg expression in clang. The following is the example program I will use in this email. I compile the program with clang targeting mips. Note that I have not pushed all the changes I have made yet, so you will not be able to see the same results. $ clang -ccc-host-triple mipsel-unknown-linux

Hello, I am still working at recovering from a few failed OS hard drives on my gluster storage and have been removing, and re-adding bricks quite a bit. I noticed yesterday night that some of the directories are not visible when I access them through the client, but are still on the brick. For example: Client: # ls /scratch/dw Ethiopian_imputation HGDP Rolwaling Tibetan_Alignment Brick: #

I was testing ocfs2 on a 2 node cluster set up. ocfs2-tools version is 1.6.3 ocfs2 kernel version is 2.6.36 Using cman on 2 nodes node02 dw # ping_pong -rwm /data/test.dat 3 data increment = 2 14 locks/sec node01 dw # ping_pong -rw /data/test.dat 3 data increment = 2 10 locks/sec node02 dw # ping_pong -r /data/test.dat 3 1980 locks/sec Using cman on 1 node node02 dw #

R-3.0.1 rev 62743, binary downloaded from CRAN just now; macosx 10.8.3 Hello, eigen(symmetric=TRUE) behaves strangely when given complex matrices. The following two lines define 'A', a 100x100 (real) symmetric matrix which theoretical considerations [Bochner's theorem] show to be positive definite: jj <- matrix(0,100,100) A <- exp(-0.1*(row(jj)-col(jj))^2) A's being

Hello all- I have two time series, Index1stdiff and Comps1stdiff. I regressed the first on the second and R returned the summary stats I expected. Then I looked at and plotted the residuals. I then wanted to assess autocorrelation characteristics and tried to run a Durbin-Watson using: library(lmtest) dwtest(formula=Index1stdiff~Comps1stdiff,alternative=c("greater")) I am

I'm fitting generalized linear mixed models to using several fixed effects (main effects and a couple of interactions) and a grouping factor (site) to explain the variation in a dichotomous response variable (family=binomial). I wanted to compare the output I obtained using PROC GLIMMIX in SAS with that obtained using lmer in R (version 2.6.1 in Windows). When using lmer I'm specifying

Hi Dariusz, Hans, I can apply the dw-hdmi patches if necessary. Neil On 19/08/2019 11:38, Hans Verkuil wrote: > Hi all, > > The patches in this series can be applied independently from each other. > > If you maintain one of these drivers and you want to merge it for v5.4 > yourself, then please do so and let me know. If you prefer I commit it > to drm-misc, then please

Hi, I have two time series, y and x. Diff(y) and Diff(x) both show no autocorrelation. But durbin.watson(lm(Diff(y)~lag(Diff(x),k=-4)) gives a DW value of zero. How come the residule is autocorrelated while Diff(y) and Diff(x) are not? Does anyone know if in my case a DW of zero indicates serial correlation, or is it telling me that the DW statistics is not the appropriate statistics to use here?