similar to: first-order integer valued autoregressive process, inar(1)

hello, I try to model traffic accidents with the following model: glm.nb(y~j+w+m+sf+b+ft,data=fr[]). the problem is that there exist autocorrelation in the data. one possibility is to model traffic accidents with inar(1)-models. has anyone an idea how to change this model in order to abtain an integer valued time series model? thanks nazli

Hello, is there anyone, who works with modelling of time series of count data. I try to model traffic accidents with inar(1)-models, but I have a lot of problems with the R-Code. I would be very grateful, if someone helped me. Nazli

Hi! The same Inar reported for rawhide (https://stat.ethz.ch/pipermail/r-devel/2023-March/082482.html) is true for SuSE's distros. Right now R does not compile with libcurl8, but SuSE Tumbleweed/Factory switched to 8 a week ago. Would be great, if the patch Inar provided could be applied to main. Detlef -- "Wozu leben wir, wenn nicht dazu, uns gegenseitig das Leben einfacher zu

On 03/04/2023 14:07, Detlef Steuer wrote: > Hi! > > The same Inar reported for rawhide > (https://stat.ethz.ch/pipermail/r-devel/2023-March/082482.html) > is true for SuSE's distros. > > Right now R does not compile with libcurl8, but SuSE Tumbleweed/Factory > switched to 8 a week ago. > > Would be great, if the patch Inar provided could be applied to >

Hello - I need to do simple linear autoregressive model with R software for my thesis. I looked into all your documentation and I am not able to find anything too helpful. Can someone help me with the codes? Thanks Emil [[alternative HTML version deleted]]

Am Mon, 3 Apr 2023 15:13:58 +0100 schrieb Prof Brian Ripley <ripley at stats.ox.ac.uk>: > On 03/04/2023 14:07, Detlef Steuer wrote: > > Hi! > > > > The same Inar reported for rawhide > > (https://stat.ethz.ch/pipermail/r-devel/2023-March/082482.html) > > is true for SuSE's distros. > > > > Right now R does not compile with libcurl8, but

On 03/04/2023 15:24, Detlef Steuer wrote: > Am Mon, 3 Apr 2023 15:13:58 +0100 > schrieb Prof Brian Ripley <ripley at stats.ox.ac.uk>: > >> On 03/04/2023 14:07, Detlef Steuer wrote: >>> Hi! >>> >>> The same Inar reported for rawhide >>> (https://stat.ethz.ch/pipermail/r-devel/2023-March/082482.html) >>> is true for SuSE's

Dear R users, I would like to simulate, for 20000 replications, an autoregressive process: y(t)=0.8*y(t-1)+e(t) where e(t) is i.i.d.(0,sigma*sigma), Thank you in advance ____________________________________________________ Écoutez gratuitement le nouveau single de Noir Désir et découvrez d'autres titres en affinité avec vos goûts musicaux

Dear R users, I am trying to find a package that can estimate an autoregressive model for discrete data. I am imagining a Poisson or Gamma process in which the mean (say mu) follows a process such as mu_t = a + b*x + c*mu_{t-1} Suppose I have data on the time-series Poisson outcomes and x and would like to obtain ML estimates for b and c. Does anyone know of a package that can do this

Greetings: I am a relatively new user to R. I was wondering if anyone is familiar with MATLAB's far_g() function. If yes, is there an R equivalent to this? I would like to have the ability to input as my observation vector continuous values. I noticed that there was something close in R, sar_probit_mcmc(), but I can only use a binary vector as my observation vector. If my

Dear List, I'm using gam in a multiple imputation framework -- specifying the knot locations, and saving the results of multiple models, each of which is fit with slightly different data (because some of it is predicted when missing). In MI, coefficients from multiple models are averaged, as are variance-covariance matrices. VCV's get an additional correction to account for how

Hello everybody, I've a question about "autoregressive Regressionmodels". Let Y[1],.....,Y[n], be a time series. Given the model: Y[t] = phi[1]*Y[t-1] + phi[2]*Y[t-1] + ... + phi[p]*Y[t-p] + x_t^T*beta + u_t, where x_t=(x[1t],x[2t],....x[mt]) and beta=(beta[1],...,beta[m]) and u_t~(0,1) I want to estimate the coefficients phi and beta. Are in R any functions or packages for

Hi, I am trying to replicate Engle and Manganelli's paper Conditional Autoregressive Value at Risk (CAViaR) by Regression Quantiles. I have the Matlab code which I cannot get to work as I have never used Matlab before, does anyone know if there is the same code available to estimate the CAViaR models in R? Thanks, Shane -- View this message in context:

Hi all ! I would like to simulate spatial lattice/areal data with a conditional autoregressive (CAR) structure, for a given neighbouring matrix and for a autocorrelation "rho". Is there any package or function in R to perform it ? I found the function "CARsimu" in the hdeco library, but this is not what I'm looking for Thanks in advance Dae-Jin --

Hello, I am using R for fitting parameters of a time series model. The model is as below. Y(t) = mu + a*X(t) + YN(t) where YN(t) = b*YN(t-1) + innovation and Z(t) follows N(0,1). The main obstacle for me is the autoregressive error term, YN(t). I can't figure out how to estimate the parameters (mu, a, b) with usual 'arima' function in R. What I have tried is.... 1. Do the

In case more of you come across my request from this morning, I've already gotten several great tips, which I summarize here since one or two of these did not come across R-help as well. A team of fellow political scientists is on this problem like "white-on-rice"! Brandt, Patrick, John T. Williams Benjamin O. Fordham, and Brian Pollins. 2000. "Dynamic Modeling for Persistent

Dear All, I have a short question. Is there any readily available function that could generate either an ARMAX model or, more generally, an AutoRegressive Distributed Lag model? I am looking for a function that is similar to armaSim() function in fArma package. Thank you. MP

folks, does anyone know if the SEMIFAR model has been implemented in R? i see that there's a S-FinMetrics function SEMIFAR() that does the job, but I have no access to that software. essentially, this semiparametric fractional autoregressive model introduces a deterministic trend to the FARIMA(p,d,0) model (which, as i understand it, takes care of the random trend and short and long memory).

I wish to estimate sparse causal networks from simulated time series data. Although there's some discussion about this problem in the literature (at least a few authors have used lasso and l(1,2) regularization to enforce sparsity in multivariate autoregressive models, e.g., http://user.cs.tu-berlin.de/~nkraemer/papers/grplasso_causality.pdf), I can't find any R packages with these

I am having difficulty fitting a mgcv::gamm model that includes both a random smooth term (i.e. 'fs' smooth) and autoregressive errors. Standard smooth terms with a factor interaction using the 'by=' option work fine. Both on my actual data and a toy example (below) I am getting the same error so am inclined to wonder if this is either a bug or a model that gamm is simply unable