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Multi-State Models for Panel Data: The msm Package for R [推广有奖]

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Eviewschen 发表于 2013-12-19 09:21:16 |显示全部楼层 |坛友微信交流群

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Authors:

Christopher Jackson

Title:

[download]
(11793)
Multi-State Models for Panel Data: The msm Package for R

Reference:

Vol. 38, Issue 8, Jan 2011Submitted 2009-07-21, Accepted 2010-08-18

Type:

Article

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.

Paper:

[download]
(11793)
Multi-State Models for Panel Data: The msm Package for R
(application/pdf, 722.8 KB)

Supplements:

[download]
(975)
msm_1.0.tar.gz: R source package
(application/x-gzip, 690.8 KB)

[download]
(1526)
v38i08.R: R example code from the paper
(application/x-tex, 64.8 KB)


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关键词:panel data package models model Panel article example process reviews status

Eviewschen 发表于 2013-12-23 21:15:33 |显示全部楼层 |坛友微信交流群
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A Common Platform for Graphical Models in R: The gRbase Package
Søren Højsgaard, Claus Dethlefsen
Vol. 14, Issue 17, Dec 2005Submitted 2005-08-10, Accepted 2005-12-03

The gRbase package is intended to set the framework for computer packages for data analysis using graphical models. The gRbase package is developed for the open source language, R, and is available for several platforms. The package is intended to be widely extendible and flexible so that package developers may implement further types of graphical models using the available methods.

The gRbase package consists of a set of S version 3 classes and associated methods for representing data and models. The package is linked to the dynamicGraph package (Badsberg 2005), an interactive graphical user interface for manipulating graphs.

In this paper, we show how these building blocks can be combined and integrated with inference engines in the special cases of hierarchical loglinear models. We also illustrate how to extend the package to deal with other types of graphical models, in this case the graphical Gaussian models.


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Eviewschen 发表于 2013-12-23 21:16:27 |显示全部楼层 |坛友微信交流群

Authors:

João Vitor Dias Monteiro, Sudipto Banerjee, Gurumurthy Ramachandran

Title:

[download]
(1518)
B2Z: R Package for Bayesian Two-Zone Models

Reference:

Vol. 43, Issue 2, Jul 2011Submitted 2010-06-09, Accepted 2011-05-12

Type:

Article

Abstract:

A primary issue in industrial hygiene is the estimation of a worker's exposure to chemical, physical and biological agents. Mathematical modeling is increasingly being used as a method for assessing occupational exposures. However, predicting exposure in real settings is constrained by lack of quantitative knowledge of exposure determinants. Recently, Zhang, Banerjee, Yang, Lungu, and Ramachandran (2009) proposed Bayesian hierarchical models for estimating parameters and exposure concentrations for the two-zone differential equation models and for predicting concentrations in a zone near and far away from the source of contamination.

Bayesian estimation, however, can often require substantial amounts of user-defined code and tuning. In this paper, we introduce a statistical software package, B2Z, built upon the R statistical computing platform that implements a Bayesian model for estimating model parameters and exposure concentrations in two-zone models. We discuss the algorithms behind our package and illustrate its use with simulated and real data examples.

Paper:

[download]
(1518)
B2Z: R Package for Bayesian Two-Zone Models
(application/pdf, 824.2 KB)

Supplements:

[download]
(363)
B2Z_1.4.tar.gz: R source package
(application/x-gzip, 32.4 KB)

[download]
(345)
v43i02.R: R example code from the paper
(application/octet-stream, 6.4 KB)

[download]
(427)
realdata.txt: Example data in ASCII format
(text/plain, 3.1 KB)

Resources:

BibTeX | OAI

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Eviewschen 发表于 2013-12-23 21:18:01 |显示全部楼层 |坛友微信交流群

Authors:

Volker J. Schmid, Leonhard Held

Title:

[download]
(5461)
Bayesian Age-Period-Cohort Modeling and Prediction - BAMP

Reference:

Vol. 21, Issue 8, Oct 2007Submitted 2007-09-04, Accepted 2007-10-01

Type:

Article

Abstract:

The software package BAMP provides a method of analyzing incidence or mortality data on the Lexis diagram, using a Bayesian version of an age-period-cohort model. A hierarchical model is assumed with a binomial model in the first-stage. As smoothing priors for the age, period and cohort parameters random walks of first and second order, with and without an additional unstructured component are available. Unstructured heterogeneity can also be included in the model. In order to evaluate the model fit, posterior deviance, DIC and predictive deviances are computed. By projecting the random walk prior into the future, future death rates can be predicted.

Paper:

[download]
(5461)
Bayesian Age-Period-Cohort Modeling and Prediction - BAMP
(application/pdf, 554.1 KB)

Supplements:

[download]
(1068)
bamp-sources.zip: BAMP source code
(application/zip, 160.9 KB)

[download]
(1120)
bamp-windows.zip: BAMP Windows binaries
(application/zip, 872 KB)

[download]
(994)
bamp-linux.tar: BAMP Linux binaries
(application/x-tar, 610 KB)

[download]
(992)
bamp-graphics.zip: R code for visualizing BAMP output
(application/zip, 2.3 KB)

Resources:

BibTeX | OAI

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Eviewschen 发表于 2013-12-23 21:20:30 |显示全部楼层 |坛友微信交流群

Authors:

Harold Doran, Douglas Bates, Paul Bliese, Maritza Dowling

Title:

[download]
(13124)
Estimating the Multilevel Rasch Model: With the lme4 Package

Reference:

Vol. 20, Issue 2, Feb 2007Submitted 2006-10-01, Accepted 2007-02-22

Type:

Article

Abstract:

Traditional Rasch estimation of the item and student parameters via marginal maximum likelihood, joint maximum likelihood or conditional maximum likelihood, assume individuals in clustered settings are uncorrelated and items within a test that share a grouping structure are also uncorrelated. These assumptions are often violated, particularly in educational testing situations, in which students are grouped into classrooms and many test items share a common grouping structure, such as a content strand or a reading passage. Consequently, one possible approach is to explicitly recognize the clustered nature of the data and directly incorporate random effects to account for the various dependencies. This article demonstrates how the multilevel Rasch model can be estimated using the functions in R for mixed-effects models with crossed or partially crossed random effects. We demonstrate how to model the following hierarchical data structures: a) individuals clustered in similar settings (e.g., classrooms, schools), b) items nested within a particular group (such as a content strand or a reading passage), and c) how to estimate a teacher x content strand interaction.

Paper:

[download]
(13124)
Estimating the Multilevel Rasch Model: With the lme4 Package
(application/pdf, 553.3 KB)

Supplements:

[download]
(2367)
v20i02.R: R example code from the paper
(application/zip, 1014 Bytes)

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Multi-State Models for Panel Data: The msm Package for R--这篇文章的下载链接和数据都没有下载按钮,可否再次分享?万分感谢!

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