On 19 Oct 2003 at 11:02, Paul Meagher wrote: You could look at chapter 5 of Jim Lindset's online document
"The statistical analysis of stochastic processes in Time", at his website, www.luc.ac.be/~jlindsey At this site there is also a collection of R functions for his examples. Kjetil Halvorsen > Can someone give me a pointer to where I should be looking for markov chain > resources in R? > > Longer term, I am also interested in the question of whether explanatory > variables can coupled to a probability transition matrix to assist in > predicting the next state that a an object/system will go into. I'm > imagining that this gets kind of ugly when you have nominal data (i.e., the > next state) that you are trying to predict using a transition matrix and you > want to try to boost your predictive power by incorporating other regressor > variables. Can this be done, how, and is there something in R that does > this? > > Another issue is how to assess the potential usefulness of the probability > transition matrix and the corresponding frequency matrix. If your frequency > matrix only has a few observations in each cell then it is not too useful > for predictive purposes. Also, if the probabilties are close to .50 in all > the cells, again it is not useful because it is not "informative" about the > next state. Is their research that speaks to the issue of assessing the > "utility" or "informativeness" of the transition matrix for predictive > purposes. > > Regards, > Paul Meagher > > ______________________________________________ > [EMAIL PROTECTED] mailing list > https://www.stat.math.ethz.ch/mailman/listinfo/r-help ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
