similar to: msm

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

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

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

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.

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

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

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 ~

We are trying to build a human respiration model. Preliminary analysis of some breathing signals has shown that humans breathe through switching among a finite number of patterns. Hidden Markov seems to be the right approach. Since most of our code is written in R scripting language, finding an R package implementing an HMM that we can use for our prototype would be very helpful. I have been

I am reading the comprehensive on-line documentation about msm. The positive side is that it seems it has been designed for biomedical statistics, like Clinical Trials. The bad side is that it does not seem to model observations sequences that are not independent but instead are autocorrelated, as it is my case. I did not find any mention to correlated observations therefore I assume the authors

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

Greetings all, I'm trying to install the msm package and it is failing on compilation. The problem seems to be the analyticp component? Any advice on how to get it to work? error message is below. I'm running R version 2.6.2 (2008-02-08) x86_64-redhat-linux-gnu on a dell precision 690 with Fedora Core 8. Thanks, Adam >

Prepare for the coming implementation by GCC and Clang of the __counted_by attribute. Flexible array members annotated with __counted_by can have their accesses bounds-checked at run-time checking via CONFIG_UBSAN_BOUNDS (for array indexing) and CONFIG_FORTIFY_SOURCE (for strcpy/memcpy-family functions). As found with Coccinelle[1], add __counted_by for struct dpu_hw_intr. [1]

Prepare for the coming implementation by GCC and Clang of the __counted_by attribute. Flexible array members annotated with __counted_by can have their accesses bounds-checked at run-time checking via CONFIG_UBSAN_BOUNDS (for array indexing) and CONFIG_FORTIFY_SOURCE (for strcpy/memcpy-family functions). As found with Coccinelle[1], add __counted_by for struct dpu_hw_intr. [1]

Prepare for the coming implementation by GCC and Clang of the __counted_by attribute. Flexible array members annotated with __counted_by can have their accesses bounds-checked at run-time checking via CONFIG_UBSAN_BOUNDS (for array indexing) and CONFIG_FORTIFY_SOURCE (for strcpy/memcpy-family functions). As found with Coccinelle[1], add __counted_by for struct dpu_hw_intr. [1]

windows XP R 2.6.0 I have tried to install the msm package several times. Each time the installation appears to work. I then go to PACKAGES->LOAD PACKAGE but the msm package does not appear in the SELECT ONE dialog box. Can someone suggest how I can get msm to run on my system? Thanks, John Confidentiality Statement: This email message, including any attachments, is for th...{{dropped:6}}

Hello, I was curious if there was a complex valued matrix exponential function available for R? I have some Laplace transforms of occupation times for a hidden Markov model. The matrix exponential function in the msm package does not seem to handle complex values. For example > MatrixExp(diag(1i,2)) [,1] [,2] [1,] 1 0 [2,] 0 1 Warning message: imaginary parts

Hi R-helpers, I am having a problem understanding how to construct the qmatrix, i have read the help menu for msm but I am still a bit lost. Below is an example of one of my transition matrices: statetable.msm(BEH, ID, data = A1) to from 1 2 3 4 5 23 200 201 203 999 1 86 11 2 20 1 9 3 11 1 22 2 18 4 4 4 0 1 1 1 0 1 3 5 2 0 5 0 0 2 0 0 3

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.