similar to: time series regression

Hi I am using Cox regression to identify at risk groups. How can I get the C-index in R? Thanks, Bereket <'r-help@stat.math.ethz.ch'> [[alternative HTML version deleted]]

Hi, I am trying to learn time series, and I am attending a colleague's course on Econometrics. However, he uses e-views, and I use R. I am trying to reproduce his examples in R, but I am having problems specifying a AR(1) model. Would anyone help me with my code? Thanks in advance! Reproducible code follows: download.file("https://sites.google.com/a/proxima.adm.br/main/ex_32.csv

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?

Hi, I am using 2 times series and I want to carry out a regression of Seri1 by Serie2 using structured (autocorrelated) errors. (Equivalent to the autoreg function in SAS) I found the function gls (package nlme) and I made: gls_mens<-gls(mening_s_des~dataATB, correlation = corAR1()) My problem is that I don’t want a AR(1) structure but ARMA(n,p) but the execution fails :

Hi there, I am using gls from the nlme library to fit an AR(1) regression model. I am wondering if (and how) I can separate the auto-correlated and random components of the residuals? Id like to be able to plot the fitted values + the autocorrelated error (i.e. phi * resid(t-1)), to compare with the observed values. I am also wondering how I might go about calculating confidence (or

Hi, I am trying to perform Maximum Likelihood estimation of a Multivariate model (2 independent variables + intercept) with autocorrelated errors of 1st order (ar(1)). Does R have a function for that? I could only find an univariate option (ar.mle function) and when writing my own I find that it is pretty memory-consuming (and sometimes wrong) so there must be a better way. Thanks, KB

Hi, I have a problem with a regression I try to run. I did an estimation of the market model with daily data. You can see to output below: /> summary(regression_resn) Time series regression with "ts" data: Start = -150, End = -26 Call: dynlm(formula = ror_resn ~ ror_spi_resn) Residuals: Min 1Q Median 3Q Max -0.0255690 -0.0030378 0.0002787

Hi, I have fitted a model using a glm() approach and using a gls() approach (but without correcting for spatially autocorrelated errors). I have noticed that although these models are the same (as they should be), the AIC value differs between glm() and gls(). Can anyone tell me why they differ? Thanks, Geertje ~~~~ Geertje van der Heijden PhD student Tropical Ecology School of Geography

Dear R users, In my data, there are two variables t1 and t2. For each observation of t1 and t2, two location indicators (x, y) were provided. The data format is # x y t1 t2 Since the both t1 and t2 are depended on x and y, t1 and t2 are autocorrelated variables. My question is how to calculate the correlation between t1 and t2 by taking into account the structure of residual variance

Hello, I am attending a course in Computational Statistics at ETH and in one of the assignments I am asked to prove that a time series is not autocorrelated using the R function "acf". I tried out the acf function with the given data, according to what I found here: http://landshape.org/enm/options-for-acf-in-r/ this test data does not look IID but rather shows some trends so how can I

Hi, I have two sets of data for a given set of (non-lattice) locations. I would like to know whether the two are significantly different. This would be simple enough if it wasn't for the fact that the data is spatially autocorrelated. I have come across several possible solutions (including Cliff & Ord which however appears to be for gridded data), or using gls. However, they don't

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

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

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

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.

Hi, how to estimate a the following model in R: y(t)=beta0+beta1*x1(t)+beta2*x2(t)+...+beta5*x5(t)+beta6*y(t-1)+beta7*y(t-2)+beta8*y(t-3) 1) using "lm" : dates &lt;- as.Date(data.df[,1]) selection&lt;-which(dates&gt;=as.Date("1986-1-1") &amp; dates&lt;=as.Date("2007-12-31")) dep &lt;- ts(data.df[selection,c("dep")]) indep.ret1

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")

I am trying to learn the gee function in R. So I try to generate some data and use this function. I have the following lines: ######################################## Gee # Generating lny=10+2*Si-Si^2+eta # eta ~ N(0,1) # Si ~ U(0,11) eta <- vector(mode="numeric",100) eta <- rnorm(100) Si <- vector(mode="numeric",100) Si <- runif(100, min=0, max=11) lny <-

Could anyone tell me whether there is an R function for removing autocorrelations from a series of observations before performing a linear or nonlinear regression analysis on them? Many thanks, Andrew Wilson

Hi I have some panel data for the 50 US states over about 25 years, and I would like to test a simple model via OLS, using this data. I know how to run OLS in R, and I think I can see how to create Panel Corrected Standard Errors using http://jackman.stanford.edu/classes/350C/pcse.r What I can't figure out is how to correct for autocorrelation over time. I have found a lot of R stuff on