similar to: simulate binary markov chain

Hi all, I have a very large binary vector, I wish to calculate the number of 1's over sliding windows. this is my very slow function slide<-function(seq,window){ n<-length(seq)-window tot<-c() tot[1]<-sum(seq[1:window]) for (i in 2:n) { tot[i]<- tot[i-1]-seq[i-1]+seq[i] } return(tot) } this works well for for reasonably sized vectors. Does

Hi all, I am new to R. Can someone please give me some hints in how to do the following things: 1- Get ONE permutation of a set. I have looked at the gregmisc package's permutations() method, but I just want to get one permutation at a time. 2- Simulate a Markov chain in R. For instance, I want to simulate the simple random walk problem, in which a person can walk randomly around 4 places.

Hi, I have a vector of start positions, and another vector of stop positions, eg start<-c(1,20,50) stop<-c(7,25,53) Is there a quick way to create a sequence from these vectors? new<-c(1,2,3,4,5,6,7,20,21,22,23,24,25,50,51,52,53) the way Im doing it at the moment is pos<-seq(start[1],stop[1]) for (i in 2:length(start)){ new<-seq(start[i],stop[i]) pos<-c(pos,new) }

Dear all, I''m working with the geometric distribution for the time being, and I''m confused. This may have more to do with statistics than R itself, but since I''m getting results from R I find counterintuitive (well, yeah, my statistical intuition has not been properly sharpened), I feel like asking. The point first: If I do > rgeom(1,prob=1) I get: [1] NaN Warning

Dear all, Using rmultinom() in a stochastic model, I found this function returns an error message 'NA in probability' for an infinite probability. Maybe, a more precise message will be helpful when debugging. > rmultinom(1, 3:5, c(1/2, 1/3, Inf)) Error in rmultinom(1, 3:5, c(1/2, 1/3, Inf)) : NA in probability vector > rmultinom(1, 3:5, c(1/2, 1/3, NA)) Error in rmultinom(1,

On 1/20/20 4:26 AM, Martin Maechler wrote: > Coming late here -- after enjoying a proper weekend ;-) -- > I have been agreeing (with Spencer, IIUC) on this for a long > time (~ 3 yrs, or more?), namely that I've come to see it as a > "design bug" that rpois() {and similar} must return return typeof() "integer". > > More strongly, I'm actually pretty

>>>>> Christophe Dutang >>>>> on Sat, 8 Apr 2023 14:21:53 +0200 writes: > Dear all, > Using rmultinom() in a stochastic model, I found this function returns an error message 'NA in probability' for an infinite probability. > Maybe, a more precise message will be helpful when debugging. >> rmultinom(1, 3:5, c(1/2, 1/3,

Ugh, sounds like competing priorities. * maintain type consistency * minimize storage (= current version, since 3.0.0) * maximize utility for large lambda (= proposed change) * keep user interface, and code, simple (e.g., it would be easy enough to add a switch that provided user control of int vs double return value) * backward compatibility On 2020-01-20 12:33 p.m., Martin Maechler

On 08/04/2023 5:53 p.m., Martin Maechler wrote: >>>>>> Christophe Dutang >>>>>> on Sat, 8 Apr 2023 14:21:53 +0200 writes: > > > Dear all, > > > Using rmultinom() in a stochastic model, I found this function returns an error message 'NA in probability' for an infinite probability. > > > Maybe, a more

>>>>> Martin Maechler >>>>> on Tue, 21 Jan 2020 09:25:19 +0100 writes: >>>>> Ben Bolker >>>>> on Mon, 20 Jan 2020 12:54:52 -0500 writes: >> Ugh, sounds like competing priorities. > indeed. >> * maintain type consistency >> * minimize storage (= current version, since 3.0.0) >> *

Hi, Is there a package that will estimate simple Markov models and hidden Markov models for discrete time processes in R? Thanks in advance, David -- =============================================================== David Kaplan, Ph.D. Professor Department of Educational Psychology University of Wisconsin - Madison Educational Sciences, Room, 1061 1025 W. Johnson Street Madison, WI 53706

Dear Users, i have this time series, the tree lines means different level, i would use a Markov switching model with two states to modelling this time series. i would obtain the relative transition matrix (2X2) the first state is above the value of 23.65 (the higher line) the second state is below the value of 23.65 You can ignore the other two lines

Hi, I am looking for a package to estimate regime switching models (states following a markov chain). I found packages for Hidden Markov Models but I am looking for something a little different: In the HMM the conditional distribution of the observations (give the state) is a known distribution (normal or others), while the package I need should allow to set a conditional distribution (given the

Hi, Does anybody know a function to simulate a Markov chain given a probability transition matrix and an initial state ? Thanks. Philippe -- -------------------------------------------------- Philippe Hup? Institut Curie - Equipe Bioinformatique 26, rue d'Ulm - 75005 PARIS France +33 (0)1 42 34 65 29 Philippe.Hupe at curie.fr <mailto:Philippe.Hupe at curie.fr>

Hello, can any one point me to R-packages (if available) which include spatial Markov Chain methods? My second question is more general but hopefully not OT: Currently we are using the software TPROGS, which let people simulate property distributions in space by some Markov Chain approaches. We face some problems due to the lack of information between distances of samples along borehole path

Dear helpers, Does anyone know about a package or a function that allows to estimate Multivariate Markov-Switching Models, like MS-VAR as introduced by Krolzig(1997) with R ? Thanks a lot!! Carlo

Dear R gurus, I have data for which I want to estimate the markov transition matrix that generated the sequence, and preferably obtain some measure of confidence for that estimation. e.g., for a series such as 1 3 4 1 2 3 1 2 1 3 4 3 2 4 2 1 4 1 2 4 1 2 4 1 2 1 2 1 3 1 I would want to get an estimate of the matrix that generated it [[originally: [,1] [,2] [,3] [,4] [1,] 0.00 0.33 0.33

Hello all, I am currently attempting to replicate data from a political science article that utilized a Markov chain transition model to predict voter turnout intention at time *t*; the data was separated into two different models based on whether prior intent was to vote or not to vote. The details don't really matter. Mostly I am curious how to run a Markov chain transition model in R,

Greetings fellow R entusiasts! We have some problems converting a computer routine written initially for Gauss to estimate a Markov Regime Switching analysis with Time Varying Transition Probability. The source code in Gauss is here: http://www.econ.washington.edu/user/cnelson/markov/programs/hmt_tvp.opt We have converted the code to R, and it's running without errors, but we have some

Hi, I am trying to use the Fisher scoring method with a geometric distribution, with p = .07, 100 observations from the geom distrib, and 10 iterations. I cannot quite get the code to work. Can anyone see the mistake? n <- 100 p <- 0.07 x <- rgeom(n, p) s <- sum(x) f <- function(x, p) p*(1-p)^x L <- function(p) p^n*(1-p)^s logL <- function(p)