similar to: markov model

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

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

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 ~

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'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

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.

Dear R users, I have been trying to obtain the MLE of the following model state 0: y_t = 2 + 0.5 * y_{t-1} + e_t state 1: y_t = 0.5 + 0.9 * y_{t-1} + e_t where e_t ~ iidN(0,1) transition probability between states is 0.2 I've generated some fake data and tried to estimate the parameters using the constrOptim() function but I can't get sensible answers using it. I've tried using

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

Hi all, I am using a discrete Hidden Markov Model with discrete observations in order to detect a sequence of integers. I am using the "hmm.discnp" package. I am using the following code: signature <- c(-89, -98, -90, -84, -77, -75, -64, -60, -58, -55, -56, -57, -57, -63, -77, -81, -82, -91, -85, -89, -93) quant <- length(-110:-6) # Initialize and train the hmm with the

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

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.

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

To: cran at r-project.org Subject: New package: pomp, inference for partially-observed Markov processes The new package 'pomp' is built around a very general realization of nonlinear partially-observed Markov processes (AKA state-space models, nonlinear stochastic dynamical systems). The user provides functions specifying the model's process and measurement components. The

To: cran at r-project.org Subject: New package: pomp, inference for partially-observed Markov processes The new package 'pomp' is built around a very general realization of nonlinear partially-observed Markov processes (AKA state-space models, nonlinear stochastic dynamical systems). The user provides functions specifying the model's process and measurement components. The

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

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

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

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