similar to: How to predict significant dominant regions of two sequence of numeric values by Hidden Markov Model

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 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, I was just wondering if there are any books on R that have applications using Hidden Markov models or anyone with tutorials/exercises that can be shared. Ideally, these would have a bio/ecological slant though not neccesarily. Any suggestions would be appreciated. Thanks! [[alternative HTML version deleted]]

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

Hi folks, I'm pleased to introduce a new package called ?aphid?, for analysis with profile hidden Markov models in R. The package contains functions for multiple and pairwise sequence alignment for both nucleic acids and proteins (preferably in the DNAbin or AAbin format), model building, parameter optimization (Baum Welch and Viterbi training), plotting, file import & export,

Hi folks, I'm pleased to introduce a new package called ?aphid?, for analysis with profile hidden Markov models in R. The package contains functions for multiple and pairwise sequence alignment for both nucleic acids and proteins (preferably in the DNAbin or AAbin format), model building, parameter optimization (Baum Welch and Viterbi training), plotting, file import & export,

Hi, friends! Has R estimation (library, for example) to do estimation in HMM? Thanks in advance, ======================================== Cezar Freitas Estatistico - Comissao Permanente para os Vestibulares / UNICAMP Probabilidade e Estatistica Aplicadas - IME / USP | IMECC / UNICAMP Campinas | Sao Paulo, SP - Brasil

R Community - I am attempting to fit a model as described in Hampton, Bossaerts, and O'doherty (J. Neuroscience) 2006. They use a bayesian hidden markov model to model the Reversal Learning data. I have tried using HMM and depmixS4 with no success. My data is a Reversal Learning Task in which there are 3 sets of patterns over 3 blocks. The participant receives incorrect or correct

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

Dear Oscar,   Thanks for your help.It's so nice of you to explain this package to me.   Best Regards,   James LAN 发件人： Oscar Rueda [via R] <ml-node+s789695n4431468h14@n4.nabble.com> 收件人： monkeylan <lanjinchi@yahoo.com.cn> 发送日期： 2012年2月29日, 星期三, 下午 9:21 主题: Re: Bayesian Hidden Markov Models Dear James, The distances are normalized between zero and 1, so in your case all of

Hello i am new to ggplot and i observed a strange behavior. I want to display two groups of points, each group with a different color. But i encountered a problem with the colors. Here is a first example: dataset <- data.frame(Main = c("A", "A", "B", "B"), Detail = c( "b", "c", "1", "2"), resp = runif(4, min

Hi all. This is perhaps more a statistics question but I'm hoping someone can help me. I have a group of patients for whom I'm looking at beat to beat RR interval changes. I have plotted the difference between one beat length and the next against the difference between the previous beat length and the current one. This gives me a plot with four quadrants: the bottom left corresponding to

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, 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>

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

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

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.