similar to: depmixS4+transition

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 the case? Also are there any alternate packages for the same? Thanks

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, 0.02173913,0.3478261,0.63043478), nrow = 3, ncol =

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

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 not observed. I see no prediction functionality anywhere,

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:

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 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]]

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 dependent mixture models. Responses or observations can be modeled using GLMs, and additionally

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 dependent mixture models. Responses or observations can be modeled using GLMs, and additionally

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 the EM algorithm, most

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 the EM algorithm, most

Hi, When running R CMD check I'm getting a number of errors that I don't quite follow and don't know where to start looking for an answer, any hints appreciated. 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

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

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?

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

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

Hi, I have a question about running multiple in regressions in R and then storing the coefficients. I have a large dataset with several variables, one of which is a state variable, coded 1-50 for each state. I'd like to run a regression of 28 select variables on the remaining 27 variables of the dataset (there are 55 variables total), and specific for each state, ie run a regression of

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]]

Can anyone suggest a package or code for modeling a hysteresis process in R? I'm currently modeling a certain dataset with a GAM using mgcv, something like gam(y~ s(x, by=z) + z, family = Gamma(link=log),data=data) and getting fits with about 9 estimated degrees of freedom in the smooth for each value of z. FWIW, z is a treatment applied to a system which should have made an improvement,

Dear R users, Is there any package for Latent Class Analysis (to be used in a clustering application) which supports mixed indicator variables (categorical and continuous)? Alternatively, is there any other clustering algorithm available that supports this type of data? Thanks in advance for your help. Regards, Lars. [[alternative HTML version deleted]]