similar to: inar(1), time series of count data

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, in my doctoral thesis i try to model time series crash count data with an inar(1)-process, but i have a few problems in writing the r-code. is there someone, who works with inar-processes. i would be very grateful, if someone gives me some ideas in writing the code. nazli

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

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 >

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 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

Hi all, I have made an overdispersion test for a data set and get the following result Overdispersion test Obs.Var/Theor.Var Statistic p-value poisson data 16.24267 47444.85 0 after deleting the outliers from the data set I get the following result Overdispersion test Obs.Var/Theor.Var Statistic p-value poisson data 16.27106 0 1 The

Hi R-helpers, I'm new to time series modelling, but my requirement seems to fall just outside the capabilities of the arima function in R. I'd like to fit an ARMA model where the variance of the disturbances is a function of some exogenous variable. So something like: Y_t = a_0 + a_1 * Y_(t-1) +...+ a_p * Y_(t-p) + b_1 * e_(t-1) +...+ b_q * e_(t-q) + e_t, where e_t ~ N(0, sigma^2_t),

Hello, I'm trying to model the entry of certain firms into a larger number of distinct markets over time. I have a short time series, but a large cross section (small T, big N). I have both time varying and non-time varying variables. Additionally, since I'm modeling entry of firms, it seems like the number of existing firms in the market at time t should depend on the number of firms at

Insightful are now taking bookings for the Financial Time Series Modelling course to be held at Carlton Terrace in London SW1 on 12th and 13th April 2005. We are also pleased to offer the 1 day Advanced Time Series Modelling course on April 19th at the same location. Extract for Financial Time Series Modelling : This two day course will provide participants with a working knowledge of a range

This is causing me great consternation, and I've spent too much time floundering around on it. My data is in the form of columns in Excel, with the first column being in m/dd/yyyy hh:mm format. The spreadsheet is complicated (headers, merged cells, lines w/o data); so I've tried various ways of exporting the data into a text file for the R processing - CSV, spaced, etc. For example:

You may want to take a look at a paper by Julia Kelsall and Scott Zeger in JRSS(C) - 1999, pp. 331-344. This paper describes a frequency domain approach to log-linear regression modeling of poisson-distributed count data, accounting for correlation and over-dispersion. There are also some S functions available to implement the methodology. Ravi. -----Original Message----- From: pauljohn at

Dear all: I'm a newbie and an amateur seeking help with forecasting the next in a non-stationary time series, with constraints of 1 (low) and 27 (high) applicable to all. What I need help with is the solution concept. The series has 439 observations as of last week. I'd like to analyze obs 1 - 30 (which are historical and therefore invariate), to solve for 31. The history: Obs 1

Insightful are now taking bookings for the Advanced Time Series Modelling course to be held at Carlton Terrace in London SW1 on 20th September. Advanced workshop Extract for Financial Time Series Modelling : The Advanced Time Series Course focuses on the most up to date theory and its application around the following topics (note that not all topics will be covered during the workshop) 1.

Hello everyone, ? had 15 minutes of data from 2017-11-02 13:30:00 to 2022-11-26 23:45:00 and number of data is 177647 ? would like to ask why my time series are less then my expectation. baslangic <- as.POSIXct("2017-11-02 13:30:00", tz = "CET") bitis <- as.POSIXct("2022-11-26 23:45:00", tz = "CET") # zaman_seti <- seq.POSIXt(from = baslangic,

Dear list, I have to look examine hourly time - series and would like to plot variable section of them using plot.zoo. Hourly time series data which looks like this: YYYY MM DD HH P-uk P-kor P-SME EPOT EREA RO R1 R2 RGES S-SNO SI SSM SUZ SLZ 2003 1 1 1 0.385 0.456 0.021 0.000 0.000 0.000 0.013 0.223 0.235 0.01 0.38

Readers, I have data with different time stamps that I wish to plot (for example): data set 1 time(hh:mm:ss),datum 01:00:00,500 01:00:15,600 01:00:30,750 01:00:45,720 01:01:00,700 01:01:15,725 01:01:30,640 01:01:45,710 data set 2 time,datum 01:00:12,20 01:01:01,55 01:01:55,22 The time interval in data set 1 does not change, but the time interval in data set 2 does change, such that for a

Dear all, Before I file this as a bug, I wanted to check if I didn't miss something. The help page of lag() says that the function returns a time series object. It actually does return something that looks like a ts object (the attribute tsp is set). But when using a vector, the class "ts" is not added to the result: > avec <- 1:10 > lag(avec) [1] 1 2 3 4 5 6 7 8