similar to: Multivariate discrete HMMs

Hi, I'm a newbie to the world of HMMs and HMMs in R. I've had a look at the hmm package and the RHmm package but I couldn't see anything straightforward on how a labelled sequential dataset with observed values and underlying states might be used to construct and train a HMM based on that data and no pre-computed values for the transition, emission or initial state distributions. Does

Hi, I need to draw hmm's in R.I also need to include a small plot near each state of the hmm.How can I do this? -- Rajesh.J [[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

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 List, We are using RHmm to cluster data through HMM. We would like to restrict the transition matrix of HMM, to get hierarchical connections between clusters. But, RHmm doesn't seem to support these restrictions. Can any one suggest a library to do that. Thanks, Kishor

I admire package-builders being proactive and having their facilities ready for upcoming R releases. However, if the publicly released version of R is 2.10.1 and a package is built for R 2.11.0, users get the embarrassing notice about a disconnect, and cannot do much about it. If it is tedious to roll out a new package on time, perhaps there's a possibility for automation here. Thanks. I

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

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

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

Dear all, I have a continuous variable that can take on values between 0 and 100, for example: x<-runif(100,0,100) I also have a second variable that defines a series of thresholds, for example: y<-c(3, 4.5, 6, 8) I would like to convert my continuous variable into a discrete one using the threshold variables: If x is between 0 and 3 the discrete variable should be 1 If x is between 3

Is there a package that will allow me to fit Brownian motion and Ornstein?Uhlenbeck models of evolution for discrete traits? I know that geiger and ouch have commands for fitting these models for continuous traits, but these aren't suitable for discrete trait evolution, correct? -- View this message in context:

Hi all, I am REALLY confused with the variance right now. for a discrete uniform distribution on [1,12] the mean is (1+12)/2=6.5 which is ok. y=1:12 mean(y) then var(y) gives me 13 1- on http://en.wikipedia.org/wiki/Uniform_distribution_%28discrete%29 wiki the variance is (12^2-1)/12=143/12 2-

Dear R users, I have a problem for which my objective function depends on both discrete and continuous arguments. The problem is that the number of combinations for the (multivariate) discrete arguments can become overwhelming (when it is univariate this is not an issue) hence search over the continuous arguments for each possible combination of the discrete arguments may not be feasible. Guided

I'm reading a old statistics book "MARVIN J. Karson (1982), Multivariate Statistical Methods, The IOWA State University Press, Iowa". In the chapter XI i can find some information about discrete multivariate analysis. This chapter is restricted to an introduction to log-linear models for analysis of multidimensional contingency tables. For example, in the log-linear model for

Hello, I am using the mda package and in particular the fda routine to classify in term of gear a set of 20 trips. I preformed a flexible discriminant analysis (FDA) using a set of 151 trips. FDAT1 <- fda(as.factor(gear) ~ . , data =matrizR) A total of 22 predictors were considered. 20 of the predictors are "numeric" and 2 are "factors/discrete". The resulting FDA

Dear Rgurus, To my knowledge the best way to visualize the distribution of a discrete variable X is plot(table(X)) The problem which I have is the following. I have to discrete variables X and Y which distribution I would like to compare. To overlay the distribution of Y with lines(table(Y)) gives not satisfying results. This is the same in case of using density or histogram. Hence, I am

Dear all, I am trying to simulate from discrete uniform distribution. But I could not find any in-built code in R. Could anyone help me please? Thanks in advance for the time and help. Cassie [[alternative HTML version deleted]]

Hi Group, I have been working with the code below. Everything seems to work okay, except that the discrete values on the x-axis are far from each end of the graph. I've tried several things including changing the discrete values and playing with the limits, but can't get it to work. I tested this on simulated data and do not have the same problem, so I guessing it is how I'm

Dear all, I have a discrete distribution showing how age is distributed across a population using a certain set of bands: Age <- matrix(c(74045062, 71978405, 122718362, 40489415), ncol=1, dimnames=list(c("<18", "18-34", "35-64", "65+"),c())) Age_dist <- Age/sum(Age) For example I know that 23.94% of all people are between 0-18 years, 23.28%

Hello list: I generate by simulation (using different procedures) two sample vectors of size N, each corresponding to a discrete variable and I want to text if these samples can be considered as having the same probability distribution (which is unknown). What is the best test for that? I've read that Kolmogorov-Smirnov and Anderson-Darling tests are restricted to continuous data