similar to: OT: Where's the new Tukey?

I am working on my thesis in which i have couple of independent variables that are categorical in nature and the depndent variable is dichotomus. Initially I run univariate analysis and added the variables with significant p-values (p<0.25) in my full model. I have three confusions. Firstly, I am looking for confounding variables by using formula "(crude beta-cofficient - adjusted

Hi useRs.. I cant figure out how to test for causality using causality() in vars package I have two datasets (A, B) and i want to test if A (Granger)cause B. How do I write the script? I dont understand ?causality. How do I get x to "contain" A and B. Further using the command VAR() to specify x, I dont either understand. Kind regards Tobias -- View this message in context:

Dear Community, this is my first programming in R and I am stuck with a problem. I have the following code which automatically calculates Granger causalities from a variable, say e.g. "bs" as below, to all other variables in the data frame: log.returns<-as.data.frame( lapply(daten, function(x) diff(log(ts(x))))) y1<-log.returns$bs y2<- log.returns[,!(names(log.returns) %in%

I am using the vars package and it calls a function causality() which then calls something called .duplicate. I had to modify the causality function slightly for my purposes and I called it my.causality() but now the .duplicate function is no longer known to the my.causality function. I'm fairly certain that this is due to my lack of expertise in R but if someone could tell me how to make the

Dear Community, For my masters thesis I need to perform a multivariate granger causality test. I have found a code for bivariate testing on this page (http://www.econ.uiuc.edu/~econ472/granger.R.txt), which I think would not be useful for the multivariate case. Does anybody know a code for a multivariate granger causality test. Thank you in advance. Best Regards -- View this message in context:

Dear Community, I am new to R and have a question concerning the causality () test in the vars package. I need to test whether, say, the variable y Granger causes the variable x, given z as a control variable. I estimated the VAR model as follows: >model<-VAR(cbind(x,y,z),p=2) Then I did the following: >causality(model, cause="y"). I thing this tests the Granger causality of

Dear R Community, I am currently student at the Vienna University of Technology writing my Diploma thesis on causality in time series and doing some analyses of time series in R. I have the following questions: (1) Is there a function in R to estimate the PARTIAL spectral coherence of a multivariate time series? If yes, how does this work? Is there an test in R if the partial spectral

Hello dudes. I'm developing VAR analysis based on suggestions made by Horváth in its paper Canonical Correlation Analysis and Wiener-Granger Causality Tests. That's the reason I'm looking for if there's any R package to develop Wiener - Granger Causality Test. Thanks a lot for your unvaluable help. Regards from Mexico [[alternative HTML version deleted]]

Hi all, If I use a moving average, it will smooth the choppy time series, but it will lead to lagging... How do I smooth timeseries without the lagging effect? Thanks!

>>>>> Serguei Sokol <sokol at insa-toulouse.fr> >>>>> on Tue, 30 May 2017 16:01:17 +0200 writes: > Le 30/05/2017 ? 09:33, Martin Maechler a ?crit : ... >> However, even after the patch, The example from the SO >> post differs from the result of Richie Cotton's >> function... > The explanation is quite simple.

Does anyone know of a package that includes the Modified Sims test [Gewerke, 1983, Sims, 1972]? This test is used in econometrics and is a kind of alternative to the Granger test [Granger, 1969], which is in the package lmtest. Thanks in advance, chris Refernces: Gewerke, J., R. Meese, and W. Dent (1983), "Comparing Alternative Tests of Causality in Temporal Systems: Analytic Results and

Dear All, I am looking for an R implementation of the convergent cross mapping method (see http://bit.ly/XN8OZX and http://www.uvm.edu/~cdanfort/csc-reading-group/sugihara-causality-science-2012.pdf ) The method is presented as an improvement over Granger causality ( http://bit.ly/XN8ydi ), but its implementation (involving shadows of multidimensional manifolds) must be quite some work...

>(though i personally use the tap ones because the core ones do not support asan) Is there a particular reason why the core ones do not support asan? > > >On Wed, Apr 29, 2015 at 1:21 PM, Sebastian Blessing ><sebastian at causality.io> wrote: >> Dear LLVMers, >> >> I am working on a project that depends on LLVM 3.6. For Homebrew, the core >> formula

Dear All, I'am looking for examples showing that correlation does not imply causality, the targeted audience consists of undergraduate students (their first year at the university but in the BioMathStat track). All practicals are under R. I was able to extract this from R datasets: ### begin data(sunspots) data(lynx) spots <- window(sunspots, freq = 1, start = 1880, end = 1900) lnx <-

Dear R Users, Can someone point me to a R package which will help me optimally choose a lag for Granger Causality testing ? Many thanks in advance, Tolga Generally, this communication is for informational purposes only and it is not intended as an offer or solicitation for the purchase or sale of any financial instrument or as an official confirmation of any transaction. In the event you are

Dear all, I am searching for a way to compute a test comparable to Chuang et al. ("Causality in Quantiles and Dynamic Stock Return-Volume Relations"). The aim of this test is to check wheter the coefficient of a quantile regression granger-causes Y in a quantile range. I have nearly computed everything but I am searching for an estimator of the density of the distribution at several

Hello all, long time lurker, first time poster. I have a situation that has been confounding me for the better part of a week. I run an internal mirror of a Centos mirror, mainly because I have lots of hosts and low bandwidth. For my 7.x hosts, since 7.5.1804 was released, when I do a ?yum update?, I get the following errors on a number of files. "Package does not match intended download.

I have two data sets, each a vector of 1000 numbers, each vector representing a distribution (i.e. 1000 numbers each of which representing a frequency at one point on a scale between 1 and 1000). For similfication, here an short version with only 5 points. a <- c(8,10,8,12,4) b <- c(7,11,8,10,5) Leaving the obvious discussion about causality aside fro a moment, I would like to see how

Hello, does R estimate belief networks to estimate chains of causality. Anyone point me to the right direction, or the most developed library? looking at DEAL>

I don't know if this is the best place to post this question but I will try anyway. I have two experiements for which I use one-way matched-randomized ANOVA for the analysis and I would like to compare different treatments in the two experiments. The only common group in the two experiments are the controls. Is there any ANOVA design that allows me to make this comparison taking into