search for: depmixs4

Dear helpers, is there any possible that transition (in depmixS4) is in scale of two variable, e.g transition=~scale(x1,x2)? If it can be, how transition of two variable (covariate time) can be worked in depmixS4-hidden markov model for time series. Many thanks, nglthu -- View this message in context: http://r.789695.n4.nabble.com/depmixS4-transition-tp4572726...

depmixS4 has reached some form of maturity and therefore we have bumped its version number to 1.0-0 which is now on CRAN: http://cran.r-project.org/web/packages/depmixS4/index.html depmixS4 fits hidden (latent) Markov models of multivariate, mixed categorical and continuous data, otherwise known as depende...

depmixS4 has reached some form of maturity and therefore we have bumped its version number to 1.0-0 which is now on CRAN: http://cran.r-project.org/web/packages/depmixS4/index.html depmixS4 fits hidden (latent) Markov models of multivariate, mixed categorical and continuous data, otherwise known as depende...

Hi I am trying to use depmixS4 package. Based on the documentation, it seems that depmix allows one to fit an HMM model based on a training data with time-varying co-variates. However, I did not find any routines which can help test the accuracy on the fitted HMM model on out-of-sample data. Can someone confirm if that is indeed...

...eathe 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 suggested both *msm* and *depmixS4.* I have no previous experience with HMMs and feel at a loss about making a sensible choice. As a novice I am more attracted by msm because of the comprehensive documentation, with worked out examples, that I am printing out. Whereas I could not find anything but the usual R function call descripti...

I am getting started with using the depmixS4 package. First, I would like to see I am very impressed with its speed and flexibility. The question I have is regarding predicting on new data. I want to fit the model on some sequences with observed responses, and then make predictions on the right end of the sequences where the responses are no...

I am running a latent class regression with 3 nominal and 2 ordinal variables using depmixS4 but the available response models do not include one for ordinal response.  How do I go about this?   Penny [[alternative HTML version deleted]]

Package news (see below for general description of functionality) depmixS4 version 1.3-0 has been released on CRAN. See the NEWS file for an overview of all changes. The most important user-visible changes are: 1) more compact pretty-printing of parameters in print/summary of (dep)mix objects (following lm/glm style of presenting results) 2) some speed improvements in t...

Package news (see below for general description of functionality) depmixS4 version 1.3-0 has been released on CRAN. See the NEWS file for an overview of all changes. The most important user-visible changes are: 1) more compact pretty-printing of parameters in print/summary of (dep)mix objects (following lm/glm style of presenting results) 2) some speed improvements in t...

Hello, I've been enjoying using the "Mixture and Hidden Markov Models in R" by Visser & Speekenbrink to learn how to apply these analyses to my own data using depmixS4. I currently have a fitted 4-state mixture model with three emissions variables and one binomial covariate (HS). I am trying to compute confidence intervals using the following code, where fmms4s is the model: fmms4Svov <- vcov(fmms4S)$vcov #this line runs fine fmms4Sse <- standardError(fm...

On Tue, 30 May 2023 17:43:31 +0000 Heather Lucas <hlucas2 at lsu.edu> wrote: > Hello, > > I've been enjoying using the "Mixture and Hidden Markov Models in R" > by Visser & Speekenbrink to learn how to apply these analyses to my > own data using depmixS4. > > I currently have a fitted 4-state mixture model with three emissions > variables and one binomial covariate (HS). I am trying to compute > confidence intervals using the following code, where fmms4s is the > model: > > fmms4Svov <- vcov(fmms4S)$vcov #this line runs f...

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 feedback. 20% of the time they receive false feedback (they are told incorrect when they were in fact correct). Once the person achieves the cr...

...preciated. R CMD check trunk * checking for working latex ... OK * using log directory '/Users/ivisser/Documents/projects/ depmixProject/depmixNew/rforge/depmix/trunk.Rcheck' * using R version 2.6.2 (2008-02-08) * checking for file 'trunk/DESCRIPTION' ... OK * this is package 'depmixS4' version '0.1' * checking package dependencies ... OK * checking if this is a source package ... OK * checking whether package 'depmixS4' can be installed ... OK * checking package directory ... OK * checking for portable file names ... OK * checking for sufficient/correct file...

...attern/type, for many consecutive cycles. therefore dwelling in the same hidden state. The more regular the breathing process is, the more predictive its signal becomes the higher its amplitude autocorrelation order. I guess my question is: can msm implement an AutoRegressive HMM ? It seems that depmixS4 can but it has a time series length constraint that I don't quite understand. Thank you in advance for your attention. Kind regards, Maura Edelweiss -----Messaggio originale----- Da: Walter Zucchini [mailto:wzucchi@uni-goettingen.de] Inviato: mar 11/11/2008 11.32 A: mauede@alice.it Oggetto:...

As an alternative to Latent GOLD, I'm wondering if anyone knows of and R package that can manage Latent Class Analysis with mixed variable types (continuous, ordinal, and nominal/binary). [[alternative HTML version deleted]]

I'm interested in the Rsolnp package. For their primary function "solnp", one example is given, and there is a reference to "unit tests". Anyone know where these can be found? Also, Rsolnp is used in a few other packages (e.g., depmixS4), but I cannot seem to find source illustrating its call sequence, and the precise definition of the functions passed. Can anyone help? Thanks, - Jan [[alternative HTML version deleted]]

I am applying a hidden markov model on joint multivariate gaussian distribution for 2 vectors. I am using the depmixS4 package in R. Specifically, I am using the following code: mod<-depmix(list(response = mom ~ mkt + p0 + p1, mkt~1), data = regvar, nstates = 2, family = list(gaussian(), gaussian()),instart = delta, trstart=Pi) It seems that depmixS4 doesnt output the covariance estimates of the parameters....

Hello, I have initial parameters for HMM model and I want to use depmixS4 package. The parameters are in the form intial_prob_matrix=matrix(c(0.07614213, 0.45177665, 0.47208122), nrow=1, ncol=3, byrow = TRUE) transition_matrix=matrix(c(0.46666667,0.46666667,0.06666667, 0.06741573,0.5617978,0.37078652, 0.02173913,0.3478261,0.630...

...s output that facilitates my using simulation to produce estimates of uncertainty in my inferences when I have new data for x, for example. I've searched R-bloggers, R-help, Google, and browsed a number of package manuals and vignettes. I've also looked at the cusp package; that and depmixS4 seem the most attractive so far, but I think it would take a bit of effort to make those work. My hysteresis problem is not quite a hidden Markov model, although the notion of putting covariates on transition parameters in depmixS4::depmix() seems promising. I've also thought of coding the mo...

Hello All, Does anyone know of a package that performs constraint-based clusters? Ideally the package could perform "Time-Ordered Clustering", a technique applied in a recent journal article by Runger, Nelson, Harnish (using MS Excel). Quote, "in our specific implementation of constrained clustering, the clustering algorithm remains agglomerative and hierarchical, but observations