Allan McCutcheon and Colin Mills
- Published in print:
- 1998
- Published Online:
- November 2003
- ISBN:
- 9780198292371
- eISBN:
- 9780191600159
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/0198292376.003.0005
- Subject:
- Political Science, Reference
Extending the basic regression model to the analysis of contingency tables, using odds and odds ratios. The worked example shows how log‐linear and latent class techniques can be assimilated into a ...
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Extending the basic regression model to the analysis of contingency tables, using odds and odds ratios. The worked example shows how log‐linear and latent class techniques can be assimilated into a single model using GLIM, LCAG, and LEM software, and how to interpret the BIC and AIC statistics.Less
Extending the basic regression model to the analysis of contingency tables, using odds and odds ratios. The worked example shows how log‐linear and latent class techniques can be assimilated into a single model using GLIM, LCAG, and LEM software, and how to interpret the BIC and AIC statistics.
Ziheng Yang
- Published in print:
- 2014
- Published Online:
- August 2014
- ISBN:
- 9780199602605
- eISBN:
- 9780191782251
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199602605.003.0004
- Subject:
- Biology, Biomathematics / Statistics and Data Analysis / Complexity Studies, Evolutionary Biology / Genetics
This chapter describes the pruning algorithm for calculating the likelihood on a tree, as well as extensions under complex substitution models, including the gamma and covarion models of rate ...
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This chapter describes the pruning algorithm for calculating the likelihood on a tree, as well as extensions under complex substitution models, including the gamma and covarion models of rate variation among sites and lineages. It discusses numerical optimization algorithms for maximum likelihood estimation. It provides a critical assessment of methods for reconstructing ancestral states for both molecular sequences and morphological characters. Finally the chapter discusses model selection in phylogenetics using the likelihood ratio test (LRT) and information criteria such as the Akaike information criterion (AIC) and Bayesian information criterion (BIC).Less
This chapter describes the pruning algorithm for calculating the likelihood on a tree, as well as extensions under complex substitution models, including the gamma and covarion models of rate variation among sites and lineages. It discusses numerical optimization algorithms for maximum likelihood estimation. It provides a critical assessment of methods for reconstructing ancestral states for both molecular sequences and morphological characters. Finally the chapter discusses model selection in phylogenetics using the likelihood ratio test (LRT) and information criteria such as the Akaike information criterion (AIC) and Bayesian information criterion (BIC).
Andy Hector
- Published in print:
- 2015
- Published Online:
- March 2015
- ISBN:
- 9780198729051
- eISBN:
- 9780191795855
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198729051.003.0011
- Subject:
- Biology, Biomathematics / Statistics and Data Analysis / Complexity Studies, Ecology
Generalized linear mixed-effects models (GLMMs) are introduced as a combination of the mixed-effects models and GLMs met in earlier chapters. The additional challenges of the analysis are explored ...
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Generalized linear mixed-effects models (GLMMs) are introduced as a combination of the mixed-effects models and GLMs met in earlier chapters. The additional challenges of the analysis are explored and the differences in the R software functions and its output are explained. Model comparison and selection can be done using information criteria or likelihood ratio tests. In addition to the AIC and BIC the DIC was devised for use with multilevel models. The options for assessing how well the model assumptions are met are reduced relative to linear models and GLMs but the most accessible current options are demonstrated.Less
Generalized linear mixed-effects models (GLMMs) are introduced as a combination of the mixed-effects models and GLMs met in earlier chapters. The additional challenges of the analysis are explored and the differences in the R software functions and its output are explained. Model comparison and selection can be done using information criteria or likelihood ratio tests. In addition to the AIC and BIC the DIC was devised for use with multilevel models. The options for assessing how well the model assumptions are met are reduced relative to linear models and GLMs but the most accessible current options are demonstrated.
Shane A. Richards
- Published in print:
- 2015
- Published Online:
- April 2015
- ISBN:
- 9780199672547
- eISBN:
- 9780191796487
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199672547.003.0004
- Subject:
- Biology, Biomathematics / Statistics and Data Analysis / Complexity Studies, Ecology
Ecologists study systems in which the biological patterns of interest are usually the result of complex webs of interactions. In such cases, many hypotheses may be proposed that might account for the ...
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Ecologists study systems in which the biological patterns of interest are usually the result of complex webs of interactions. In such cases, many hypotheses may be proposed that might account for the patterns observed, and the problem is to determine which hypotheses are indeed consistent with the data. This chapter introduces the concept of a likelihood function, which is a way of formalizing a biological hypothesis as a mathematical model and linking it to a sampling design. It then explains how likelihood functions can be used to select among competing models, focusing on the information-theoretic approach to model selection. Examples are provided that illustrate key concepts, and demonstrate good practice when performing model selection.Less
Ecologists study systems in which the biological patterns of interest are usually the result of complex webs of interactions. In such cases, many hypotheses may be proposed that might account for the patterns observed, and the problem is to determine which hypotheses are indeed consistent with the data. This chapter introduces the concept of a likelihood function, which is a way of formalizing a biological hypothesis as a mathematical model and linking it to a sampling design. It then explains how likelihood functions can be used to select among competing models, focusing on the information-theoretic approach to model selection. Examples are provided that illustrate key concepts, and demonstrate good practice when performing model selection.
Jeffrey S. Racine
- Published in print:
- 2019
- Published Online:
- January 2019
- ISBN:
- 9780190900663
- eISBN:
- 9780190933647
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780190900663.003.0006
- Subject:
- Economics and Finance, Econometrics
This chapter covers model selection methods and model averaging methods. It relies on knowledge of solving a quadratic program which is outlined in an appendix.
This chapter covers model selection methods and model averaging methods. It relies on knowledge of solving a quadratic program which is outlined in an appendix.
