similar to: programming continuous-time markov model likelihood

Dear all, I need to estimate intensity and probability matrices by the continuous-time markov model... in a first time I used "msm" and it works perfectly. Nevertheless, I have to do other estimations that need programming the likelihood. Is it possible to see somewhere the likelihood used in the msm package? I have readen the papers about likelihood estimation of continuous-time

I'm working with a dynamic system that I've started to analyse using msm(I've emailed to chris, orignator of the program, separately, but maybe he's on holiday). I'd like to know if anyone has considered the relation between the markov analysis done with msm on the one hand, and dynamic system analysis done with MATLAB? That is, I'm working with a set of empirical data that

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

Hello, I'm working with a dynamic system that I've started to analyse using msm(I've emailed to chris, orignator of the program, separately, but maybe he's on holiday). The data is obtained from a large cohort of students and consists of a model of learning states that students pass through over a period of one school year. As analyzed with the msm program, the data shows 'no

I solved the problem in one more (and more elegant) way. So here's the program again. Where does R stand on the Anderson-Goodman test of 1957? I hunted around and nobody seems to be doing this in R. Is it that there has been much progress after 1957 and nobody uses it anymore? # Problem statement: # # You are holding a dataset where firms are observed for a fixed # (and small) set of years.

Hello Chris, I am trying to fit a 4 state multi-state model using hidden markov routine in the msm package. 1. initial parameters: twoway4.q <- rbind(c(0, 0.25, 0, 0.25), c(0.166, 0, 0.166, 0.166), c(0, 0.25, 0, 0.25), c(0, 0, 0, 0)) ematrix <- rbind( c(0, 0.01, 0, 0), c(0.01, 0, 0.01,0), c(0, 0.1, 0, 0), c(0, 0, 0, 0)) 2. the model: msm_covariates_sexandage <- msm(state ~

Dear R community, I am looking for an R package or other software to study hidden Markov models. I need to be able to incorporate multivariate emissions and covariates for the transition probabilities. The msm package seems almost perfect for my purpose, but I do not think it allows multivariate emissions. I will be grateful for your suggestions. All the best, -- Emilio A. Laca One

Dear all, I would like some help to writing the likelihood function for the continuous-time markov model, even if it can be calculated with the "msm" package, I need to know how it is calculated Thank you Luis [[alternative HTML version deleted]]

Dear All, I am a bit struggling with the many packages for Markov models available in R. Apologies for now posting a code snippet, but I am looking for some guidance here. Please consider a set like the one below (which you can get with data<-read.csv('http://dl.dropboxusercontent.com/u/5685598/data_table.csv') ). ID therapy age1 age2 EFS 7308 ormo_lunga 78

I have the following data of continuous time Markov chain. Time - [1] 0.00000000 0.01219893 0.35903929 0.69378720 0.77247183 1.56008543 [7] 1.80607724 2.59023990 2.87196272 3.05311707 3.14737319 3.20758500 [13] 3.26668915 3.42428440 3.53324567 3.83668537 3.96784473 4.17196149 [19] 4.29982361 4.46163281 4.78519834 5.20381355 5.65441135 5.78623851 [25] 6.07602075 6.10351864 6.37001057 6.62859413

A major update of the "msm" package for continuous-time Markov and hidden Markov multi-state models is now available from CRAN. Hidden Markov models with general, continuous response distributions are now supported. These models are used for Markov processes which can only be observed through the value of some noisy marker. Censored states are now supported at any observation time.

A major update of the "msm" package for continuous-time Markov and hidden Markov multi-state models is now available from CRAN. Hidden Markov models with general, continuous response distributions are now supported. These models are used for Markov processes which can only be observed through the value of some noisy marker. Censored states are now supported at any observation time.

I have (below) an attempt at an R script to find the limit distribution of a continuous-time Markov process, using the formulae outlined at http://www.uwm.edu/~ziyu/ctc.pdf, page 5. First, is there a better exposition of a practical algorithm for doing this? I have not found an R package that does this specifically, nor anything on the web. Second, the script below will give the right

Hello, I'm writing about msm. It may be that consistent users of Markov models have a good idea as to what constitutes workable data for a model. I think of general rules, in basic statistical studies where n is limited to exclude fairly precise figures in the lower range. On the other hand Markov models don't seem to be often enough used for parameters to be as well laid out. I also

Dear all, I'm trying to estimate some parameters with the optim() function but I need to restrict one parameter and I have not found how to do it. Could you help me please? my program is basically fn<-function(s) initial<-function(r) { cst<-r[1] cst1<-r[2] beta<-r[3] rho<-r[4] p1<-r[5] return(-sum()) } parms<-c() m0<-optim() I need to specify

Dear list, This question is about Hidden Markov Model. Given a transition matrix, an emission matrix and a sequence of observed symbols (actually, nucleotide sequences, A, T, C and G), I hope to predict the sequence of state by Viterbi algorithm. I searched R repository for related packages. msm package has function viterbi.msm (as well as very good document), but it only works for

Greetings, I'm using MSM (mutli-state markov modeling) package to study the progression of fibrosis in U.S hepatitis C population. I find this is a very fascinating tool for an applied researcher like myself. I have a four stage progression only model without any absorbing stage, also assuming no misclassification error in the data for the time being. I also have a couple covariates in the

Hello, I'm wondering if anyone has used the msm package to compute the steady state probabilities for a Markov model? thanks, Russell

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

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