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Title:

FlexMix: A General Framework for Finite Mixture Models and Latent Class Regression in R

Publisher:

University of California at Los Angeles, Department of Statistics

Year of Publication:

2004-01-01T00:00:00Z

Document Type:

article

Language:

English

Subjects:

LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H

LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H Minimize

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http://www.jstatsoft.org/v11/i08/paper

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Title:

FlexMix: A General Framework for Finite Mixture Models and Latent Class Regression in R

Description:

FlexMix implements a general framework for fitting discrete mixtures of regression models in the R statistical computing environment: three variants of the EM algorithm can be used for parameter estimation, regressors and responses may be multivariate with arbitrary dimension, data may be grouped, e.g., to account for multiple observations per i...

FlexMix implements a general framework for fitting discrete mixtures of regression models in the R statistical computing environment: three variants of the EM algorithm can be used for parameter estimation, regressors and responses may be multivariate with arbitrary dimension, data may be grouped, e.g., to account for multiple observations per individual, the usual formula interface of the S language is used for convenient model specification, and a modular concept of driver functions allows to interface many different types of regression models. Existing drivers implement mixtures of standard linear models, generalized linear models and model-based clustering. FlexMix provides the E-step and all data handling, while the M-step can be supplied by the user to easily define new models. Minimize

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article

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Title:

FlexMix Version 2: Finite Mixtures with Concomitant Variables and Varying and Constant Parameters

Description:

flexmix provides infrastructure for flexible fitting of finite mixture models in R using the expectation-maximization (EM) algorithm or one of its variants. The functionality of the package was enhanced. Now concomitant variable models as well as varying and constant parameters for the component specific generalized linear regression models can ...

flexmix provides infrastructure for flexible fitting of finite mixture models in R using the expectation-maximization (EM) algorithm or one of its variants. The functionality of the package was enhanced. Now concomitant variable models as well as varying and constant parameters for the component specific generalized linear regression models can be fitted. The application of the package is demonstrated on several examples, the implementation described and examples given to illustrate how new drivers for the component specific models and the concomitant variable models can be defined. Minimize

Publisher:

University of California, Los Angeles

Year of Publication:

2008-09-01T00:00:00Z

Source:

Journal of Statistical Software, Vol 28, Iss 4 (2008)

Journal of Statistical Software, Vol 28, Iss 4 (2008) Minimize

Document Type:

article

Language:

English

Subjects:

LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC...

LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H Minimize

DDC:

310 Collections of general statistics *(computed)*

Rights:

CC BY

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http://www.jstatsoft.org/v28/i04/paper

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Title:

R Version 2.0.0

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article

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Title:

Exploratory analysis of benchmark experiments an interactive approach

Description:

Benchmark experiment, Visualization, Interactive data analysis

Benchmark experiment, Visualization, Interactive data analysis Minimize

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article

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Title:

Identifiability of Finite Mixtures of Multinomial Logit Models with Varying and Fixed Effects

Description:

Conditional logit, Finite mixture, Identifiability, Multinomial logit, Unobserved heterogeneity

Conditional logit, Finite mixture, Identifiability, Multinomial logit, Unobserved heterogeneity Minimize

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article

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Title:

Evaluation of structure and reproducibility of cluster solutions using the bootstrap

Description:

Cluster analysis, Mixture models, Bootstrap

Cluster analysis, Mixture models, Bootstrap Minimize

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article

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Title:

FlexMix Version 2: Finite Mixtures with Concomitant Variables and Varying and Constant Parameters

Description:

flexmix provides infrastructure for flexible fitting of finite mixture models in R using the expectation-maximization (EM) algorithm or one of its variants. The functionality of the package was enhanced. Now concomitant variable models as well as varying and constant parameters for the component specific generalized linear regression models can ...

flexmix provides infrastructure for flexible fitting of finite mixture models in R using the expectation-maximization (EM) algorithm or one of its variants. The functionality of the package was enhanced. Now concomitant variable models as well as varying and constant parameters for the component specific generalized linear regression models can be fitted. The application of the package is demonstrated on several examples, the implementation described and examples given to illustrate how new drivers for the component specific models and the concomitant variable models can be defined. Minimize

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Title:

Bagged Clustering

Description:

| A new ensemble method for cluster analysis is introduced, which can be interpreted in two dierent ways: As complexity-reducing preprocessing stage for hierarchical clustering and as combination procedure for several partitioning results. The basic idea is to locate and combine structurally stable cluster centers and/or prototypes. Random eects...

| A new ensemble method for cluster analysis is introduced, which can be interpreted in two dierent ways: As complexity-reducing preprocessing stage for hierarchical clustering and as combination procedure for several partitioning results. The basic idea is to locate and combine structurally stable cluster centers and/or prototypes. Random eects of the training set are reduced by repeatedly training on resampled sets (bootstrap samples). We discuss the algorithm both from a more theoretical and an applied point of view and demonstrate it on several data sets. Keywords| cluster analysis, bagging, bootstrap samples, k-means, learning vector quantization I. Introduction Clustering is an old data analysis problem and numerous methods have been developed to solve this task. Most of the currently popular clustering techniques fall into one of the following two major categories: Partitioning Methods Hierarchical Methods Both methods have in common that they try to group the data s. Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-15

Source:

http://www.ci.tuwien.ac.at/~leisch/docs/papers/wp51.ps.gz

http://www.ci.tuwien.ac.at/~leisch/docs/papers/wp51.ps.gz Minimize

Document Type:

text

Language:

en

Subjects:

bagging ; bootstrap samples ; k-means ; learning vector quantization

bagging ; bootstrap samples ; k-means ; learning vector quantization Minimize

DDC:

004 Data processing & computer science *(computed)*

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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Title:

Convert Binary Correlation Matrix to Matrix of Joint Probabilities Description

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Description:

R topics documented: bincorr2commonprob. 2 check.commonprob. 3 commonprob2sigma. 4 condprob. 5 ra2ba. 5 rmvbin. 6 simul.commonprob. 7

R topics documented: bincorr2commonprob. 2 check.commonprob. 3 commonprob2sigma. 4 condprob. 5 ra2ba. 5 rmvbin. 6 simul.commonprob. 7 Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2013-07-24

Source:

http://cran.r-project.org/web/packages/bindata/bindata.pdf

http://cran.r-project.org/web/packages/bindata/bindata.pdf Minimize

Document Type:

text

Language:

en

Subjects:

Description

Description Minimize

Rights:

Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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