similar to: Updating a Markov Chain

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>

Could anyone tell me where can I find some examples of the applications to economics of a Markov chain? Many thanks in advance. Luis Rivera. -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not

Hello, My name is Maria, MBA student in Sanfransisco, USA. In my credit scoring class, I have hard time making "transition matrix", which explains probability especially in relation to "Markov chain model". It is regarding people's monthly credit payment behavior. Does R have function to caculate it? I am actually a novice in using 'R'. Please help me!!! Maria

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

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.

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,

Dear help group, Just fyi a markov chain is a sequence of transitions between states (say A,T,G,C - on a gene) with a given probability for each transition. In this case there'd be 16 different kinds, each with a different weight. Given a transition matrix (4x4) filled with all transition probabilities of course, how can I generate a random sequence of a given length, say 200?

Dear experts, I have to generate 1000 transitions of a discrete time Markov Chain and then give and estimation of the equilibrium distribution. I know the transition matrix. 0.3 0.7 0 0.2 0.5 0.3 0 0.4 0.6 Do you know how to do it with R? Thanks a lot! Estefania [[alternative HTML version deleted]]

Does anyone have any "R" code for computing the entropy of a simple first or second order Markov chain, given a transition matrix something like the following (or the symbol vector from which it is computed)? AGRe ARIe CSRe DIRe DSCe eos HRMe SPTe TOBe AGRe 0.0000000 0.0000000 0.0000000 0.0000000 1.0000000 0.0000000 0.0000000 0.0000000

Hi all, I was hoping somebody may know of a function for simulating a large binary sequence (length >10 million) using a (1st order) markov model with known (2x2) transition matrix. It needs to be reasonably fast. I have tried the following; mc<-function(sq,P){ s<-c() x<-row.names(P) n<-length(sq) p1<-sum(sq)/n s[1] <- rbinom(1,1,p1); for ( i in 2:n){ s[i]

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, 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 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

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

First, I don't have the correct lingo for this topic, so I can't really find a solution for my problem. And maybe I formulate it incorrectly, so bear with me. How would I calculate a 'constant transition matrix' if I know a given value at a given time? Let's say I know that my value is 54,0 at t=12. How do I get the initial chain value? t markov.growth 00 00.0 01 06.3

I need to find a computationally simple process for the movement of interest rates. In this simplified model, an interest rate can have 3--5 possible values, and its movement is characterized by a matrix of transition probabilities (ie, it is a Markov process). I would like to estimate this process from a given set of data. For example, let the interest rate time series be: 7 3 8 2 5 9 6

Hello list: (This is probably a stupid question).  Is there a "quick and easy" way to confirm the gauss-markov conditions of a linear multiple regression model?  That the mean of the residuals is 0 can easily be tested for. The normality of the residuals as well (shapiro-wilk?).  But what about homoscedasticity? And independence of residuals with respect to the model variables? Thanks

Thank you for your prompt answer. The breathing signal observations are the amplitude values as a function of time and phase. According to our model the hidden states are the different breathing types. Subjects, whose respiratiion process is regular, are likely to breathe, keeping the same cycle pattern/type, for many consecutive cycles. therefore dwelling in the same hidden state. The more