经管之家App
让优质教育人人可得
立即打开
Fit certain versions of the Latent Markov model for longitudinal categorical data.
| Version: | 2.2 |
| Depends: | R (≥ 2.0.0), MASS, MultiLCIRT, stats |
| Published: | 2016-02-25 |
| Author: | Francesco Bartolucci, Silvia Pandolfi - University of Perugia (IT) |
| Maintainer: | Francesco Bartolucci <bart at stat.unipg.it> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| CRAN checks: | LMest results |
| Reference manual: | LMest.pdf |
| Package source: | LMest_2.2.tar.gz |
| Windows binaries: | r-devel: LMest_2.2.zip, r-release: LMest_2.2.zip, r-oldrel: LMest_2.2.zip |
| OS X Snow Leopard binaries: | r-release: LMest_2.2.tgz, r-oldrel: LMest_2.1.tgz |
| OS X Mavericks binaries: | r-release: LMest_2.2.tgz |
| Old sources: | LMest archive |
Authors: | Ingmar Visser, Maarten Speekenbrink | ||||
Title: | depmixS4: An R Package for Hidden Markov Models | ||||
Abstract: | depmixS4 implements a general framework for defining and estimating dependent mixture models in the R programming language. This includes standard Markov models, latent/hidden Markov models, and latent class and finite mixture distribution models. The models can be fitted on mixed multivariate data with distributions from the glm family, the (logistic) multinomial, or the multivariate normal distribution. Other distributions can be added easily, and an example is provided with the exgaus distribution. Parameters are estimated by the expectation-maximization (EM) algorithm or, when (linear) constraints are imposed on the parameters, by direct numerical optimization with the Rsolnp or Rdonlp2 routines. | ||||
Page views:: 10717. Submitted: 2009-08-19. Published: 2010-08-05. | |||||
Paper: | depmixS4: An R Package for Hidden Markov Models [size=1em]Download PDF (Downloads: 10687) | ||||
Supplements: |
|
Authors: | Agnieszka Król, Philippe Saint-Pierre | ||||
Title: | SemiMarkov: An R Package for Parametric Estimation in Multi-State Semi-Markov Models | ||||
Abstract: | Multi-state models provide a relevant tool for studying the observations of a continuous-time process at arbitrary times. Markov models are often considered even if semi-Markov are better adapted in various situations. Such models are still not frequently applied mainly due to lack of available software. We have developed the R package SemiMarkov to fit homogeneous semi-Markov models to longitudinal data. The package performs maximum likelihood estimation in a parametric framework where the distributions of the sojourn times can be chosen between exponential, Weibull or exponentiated Weibull. The package computes and displays the hazard rates of sojourn times and the hazard rates of the semi-Markov process. The effects of covariates can be studied with a Cox proportional hazards model for the sojourn times distributions. The number of covariates and the distribution of sojourn times can be specified for each possible transition providing a great flexibility in a models definition. This article presents parametric semi-Markov models and gives a detailed description of the package together with an application to asthma control. | ||||
Page views:: 790. Submitted: 2013-05-14. Published: 2015-08-27. | |||||
Paper: | SemiMarkov: An R Package for Parametric Estimation in Multi-State Semi-Markov Models [size=1em]Download PDF (Downloads: 814) | ||||
Supplements: |
|
Authors: | Christopher Jackson | ||||
Title: | Multi-State Models for Panel Data: The msm Package for R | ||||
Abstract: | Panel data are observations of a continuous-time process at arbitrary times, for example, visits to a hospital to diagnose disease status. Multi-state models for such data are generally based on the Markov assumption. This article reviews the range of Markov models and their extensions which can be fitted to panel-observed data, and their implementation in the msm package for R. Transition intensities may vary between individuals, or with piecewise-constant time-dependent covariates, giving an inhomogeneous Markov model. Hidden Markov models can be used for multi-state processes which are misclassified or observed only through a noisy marker. The package is intended to be straightforward to use, flexible and comprehensively documented. Worked examples are given of the use of msm to model chronic disease progression and screening. Assessment of model fit, and potential future developments of the software, are also discussed. | ||||
Page views:: 18164. Submitted: 2009-07-21. Published: 2011-01-04. | |||||
Paper: | Multi-State Models for Panel Data: The msm Package for R [size=1em]Download PDF (Downloads: 18384) | ||||
Supplements: |
|
Authors: | Jared O\'Connell, Søren Højsgaard | ||||
Title: | Hidden Semi Markov Models for Multiple Observation Sequences: The mhsmm Package for R | ||||
Abstract: | This paper describes the R package mhsmm which implements estimation and prediction methods for hidden Markov and semi-Markov models for multiple observation sequences. Such techniques are of interest when observed data is thought to be dependent on some unobserved (or hidden) state. Hidden Markov models only allow a geometrically distributed sojourn time in a given state, while hidden semi-Markov models extend this by allowing an arbitrary sojourn distribution. We demonstrate the software with simulation examples and an application involving the modelling of the ovarian cycle of dairy cows. | ||||
Page views:: 8419. Submitted: 2009-03-02. Published: 2011-03-09. | |||||
Paper: | Hidden Semi Markov Models for Multiple Observation Sequences: The mhsmm Package for R [size=1em]Download PDF (Downloads: 8602) | ||||
Supplements: |
|
Authors: | Andre Berchtold | ||
Title: | Markov Chain Computation for Homogeneous and Non-homogeneous Data: MARCH 1.1 Users Guide | ||
Abstract: | MARCH is a free software for the computation of different types of Markovian models including homogeneous Markov Chains, Hidden Markov Models (HMMs) and Double Chain Markov Models (DCMMs). The main characteristic of this software is the implementation of a powerful optimization method for HMMs and DCMMs combining a genetic algorithm with the standard Baum-Welch procedure. MARCH is distributed as a set of Matlab functions running under Matlab 5 or higher on any computing platform. A PC Windows version running independently from Matlab is also available. | ||
Page views:: 8289. Submitted: 2000-09-04. Published: 2001-03-27. | |||
Paper: | Markov Chain Computation for Homogeneous and Non-homogeneous Data: MARCH 1.1 Users Guide [size=1em]Download PDF (Downloads: 8300) | ||
Supplements: |
|
Authors: | Yanyan Sheng | ||||||
Title: | Markov Chain Monte Carlo Estimation of Normal Ogive IRT Models in MATLAB | ||||||
Abstract: | Modeling the interaction between persons and items at the item level for binary response data, item response theory (IRT) models have been found useful in a wide variety of applications in various fields. This paper provides the requisite information and description of software that implements the Gibbs sampling procedures for the one-, two- and three-parameter normal ogive models. The software developed is written in the MATLAB package IRTuno. The package is flexible enough to allow a user the choice to simulate binary response data, set the number of total or burn-in iterations, specify starting values or prior distributions for model parameters, check convergence of the Markov chain, and obtain Bayesian fit statistics. Illustrative examples are provided to demonstrate and validate the use of the software package. The m-file v25i08.m is also provided as a guide for the user of the MCMC algorithms with the three dichotomous IRT models. | ||||||
Page views:: 6601. Submitted: 2007-09-05. Published: 2008-04-03. | |||||||
Paper: | Markov Chain Monte Carlo Estimation of Normal Ogive IRT Models in MATLAB [size=1em]Download PDF (Downloads: 6621) | ||||||
Supplements: |
|
Title: | A MATLAB Package for Markov Chain Monte Carlo with a Multi-Unidimensional IRT Model | ||||||
Abstract: | Unidimensional item response theory (IRT) models are useful when each item is designed to measure some facet of a unified latent trait. In practical applications, items are not necessarily measuring the same underlying trait, and hence the more general multi-unidimensional model should be considered. This paper provides the requisite information and description of software that implements the Gibbs sampler for such models with two item parameters and a normal ogive form. The software developed is written in the MATLAB package IRTmu2no. The package is flexible enough to allow a user the choice to simulate binary response data with multiple dimensions, set the number of total or burn-in iterations, specify starting values or prior distributions for model parameters, check convergence of the Markov chain, as well as obtain Bayesian fit statistics. Illustrative examples are provided to demonstrate and validate the use of the software package. | ||||||
Page views:: 13416. Submitted: 2008-04-22. Published: 2008-11-17. | |||||||
Paper: | A MATLAB Package for Markov Chain Monte Carlo with a Multi-Unidimensional IRT Model [size=1em]Download PDF (Downloads: 13480) | ||||||
Supplements: |
|
Authors: | Andrew D. Martin, Kevin M. Quinn, Jong Hee Park | ||||||
Title: | MCMCpack: Markov Chain Monte Carlo in R | ||||||
Abstract: | We introduce MCMCpack, an R package that contains functions to perform Bayesian inference using posterior simulation for a number of statistical models. In addition to code that can be used to fit commonly used models, MCMCpack also contains some useful utility functions, including some additional density functions and pseudo-random number generators for statistical distributions, a general purpose Metropolis sampling algorithm, and tools for visualization. | ||||||
Page views:: 15785. Submitted: 2007-01-15. Published: 2011-06-14. | |||||||
Paper: | MCMCpack: Markov Chain Monte Carlo in R [size=1em]Download PDF (Downloads: 15904) | ||||||
Supplements: |
|
Authors: | Martin D. King, Fernando Calamente, Chris A. Clark, David G. Gadian | ||
Title: | Markov Chain Monte Carlo Random Effects Modeling in Magnetic Resonance Image Processing Using the BRugs Interface to WinBUGS | ||
Abstract: | A common feature of many magnetic resonance image (MRI) data processing methods is the voxel-by-voxel (a voxel is a volume element) manner in which the processing is performed. In general, however, MRI data are expected to exhibit some level of spatial correlation, rendering an independent-voxels treatment inefficient in its use of the data. Bayesian random effect models are expected to be more efficient owing to their information-borrowing behaviour. To illustrate the Bayesian random effects approach, this paper outlines a Markov chain Monte Carlo (MCMC) analysis of a perfusion MRI dataset, implemented in R using the BRugs package. BRugs provides an interface to WinBUGS and its GeoBUGS add-on. WinBUGS is a widely used programme for performing MCMC analyses, with a focus on Bayesian random effect models. A simultaneous modeling of both voxels (restricted to a region of interest) and multiple subjects is demonstrated. Despite the low signal-to-noise ratio in the magnetic resonance signal intensity data, useful model signal intensity profiles are obtained. The merits of random effects modeling are discussed in comparison with the alternative approaches based on region-of-interest averaging and repeated independent voxels analysis. This paper focuses on perfusion MRI for the purpose of illustration, the main proposition being that random effects modeling is expected to be beneficial in many other MRI applications in which the signal-to-noise ratio is a limiting factor. | ||
Page views:: 3298. Submitted: 2010-10-01. Published: 2011-10-27. | |||
Paper: | Markov Chain Monte Carlo Random Effects Modeling in Magnetic Resonance Image Processing Using the BRugs Interface to WinBUGS [size=1em]Download PDF (Downloads: 3337) | ||
Supplements: |
|
Authors: | Artur Araújo, Luís Meira-Machado, Javier Roca-Pardiñas | ||||
Title: | TPmsm: Estimation of the Transition Probabilities in 3-State Models | ||||
Abstract: | One major goal in clinical applications of multi-state models is the estimation of transition probabilities. The usual nonparametric estimator of the transition matrix for non-homogeneous Markov processes is the Aalen-Johansen estimator (Aalen and Johansen 1978). However, two problems may arise from using this estimator: first, its standard error may be large in heavy censored scenarios; second, the estimator may be inconsistent if the process is non-Markovian. The development of the R package TPmsm has been motivated by several recent contributions that account for these estimation problems. Estimation and statistical inference for transition probabilities can be performed using TPmsm. The TPmsm package provides seven different approaches to three-state illness-death modeling. In two of these approaches the transition probabilities are estimated conditionally on current or past covariate measures. Two real data examples are included for illustration of software usage. | ||||
Page views:: 2160. Submitted: 2012-07-05. Published: 2014-12-25. | |||||
Paper: | TPmsm: Estimation of the Transition Probabilities in 3-State Models [size=1em]Download PDF (Downloads: 2382) | ||||
Supplements: |
|
Authors: | Nicole Ferguson, Somnath Datta, Guy Brock | ||||
Title: | msSurv: An R Package for Nonparametric Estimation of Multistate Models | ||||
Abstract: | We present an R package, msSurv, to calculate the marginal (that is, not conditional on any covariates) state occupation probabilities, the state entry and exit time distributions, and the marginal integrated transition hazard for a general, possibly non-Markov, multistate system under left-truncation and right censoring. For a Markov model, msSurv also calculates and returns the transition probability matrix between any two states. Dependent censoring is handled via modeling the censoring hazard through observable covariates. Pointwise confidence intervals for the above mentioned quantities are obtained and returned for independent censoring from closed-form variance estimators and for dependent censoring using the bootstrap. | ||||
Page views:: 4452. Submitted: 2011-02-08. Published: 2012-09-22. | |||||
Paper: | msSurv: An R Package for Nonparametric Estimation of Multistate Models [size=1em]Download PDF (Downloads: 4483) | ||||
Supplements: |
|
Authors: | Martin D. King, Fernando Calamente, Chris A. Clark, David G. Gadian | ||
Title: | Markov Chain Monte Carlo Random Effects Modeling in Magnetic Resonance Image Processing Using the BRugs Interface to WinBUGS | ||
Abstract: | A common feature of many magnetic resonance image (MRI) data processing methods is the voxel-by-voxel (a voxel is a volume element) manner in which the processing is performed. In general, however, MRI data are expected to exhibit some level of spatial correlation, rendering an independent-voxels treatment inefficient in its use of the data. Bayesian random effect models are expected to be more efficient owing to their information-borrowing behaviour. To illustrate the Bayesian random effects approach, this paper outlines a Markov chain Monte Carlo (MCMC) analysis of a perfusion MRI dataset, implemented in R using the BRugs package. BRugs provides an interface to WinBUGS and its GeoBUGS add-on. WinBUGS is a widely used programme for performing MCMC analyses, with a focus on Bayesian random effect models. A simultaneous modeling of both voxels (restricted to a region of interest) and multiple subjects is demonstrated. Despite the low signal-to-noise ratio in the magnetic resonance signal intensity data, useful model signal intensity profiles are obtained. The merits of random effects modeling are discussed in comparison with the alternative approaches based on region-of-interest averaging and repeated independent voxels analysis. This paper focuses on perfusion MRI for the purpose of illustration, the main proposition being that random effects modeling is expected to be beneficial in many other MRI applications in which the signal-to-noise ratio is a limiting factor. | ||
Page views:: 3298. Submitted: 2010-10-01. Published: 2011-10-27. | |||
Paper: | Markov Chain Monte Carlo Random Effects Modeling in Magnetic Resonance Image Processing Using the BRugs Interface to WinBUGS [size=1em]Download PDF (Downloads: 3337) | ||
Supplements: |
|
扫码加好友，拉您进群
全部版块
我的主页
收藏
