SAGA-package: Software for the Analysis of Genetic Architecture SAGA
In coleoguy/SAGA2: Software for the Analysis of Genetic Architecture
SAGA-package R Documentation
Software for the Analysis of Genetic Architecture
SAGA
Description
Provides multimodel parameter estimates of genetic architecture incorporating the impact of environmental and sex effects. Uses weighted least squares to solve all possible models. Chooses the model or models that are most appropriate based on AICc weights and produces model weighted parameter estimates and unconditional standard errors. A function to visualize the distribution of model fits across model space allows users to graphically see how well defined the "true" model is with their dataset.
Details
Package: SAGA
Version: 2.0.0
Author(s)
Heath Blackmon
Jeffery P. Demuth
Andrew Armstrong
Jonathon Leach
Correspondence: Heath Blackmon at coleoguy@gmail.com
References
Blackmon, H. and J. Demuth. 2016. An information-theoretic approach to estimating the composite genetic effects contributing to variation among generation means: Moving beyond the joint-scaling test for line cross analysis. Evolution 70:2 420-432.
Lynch, M. and B. Walsh. 1998. Genetics and analysis of quantitative traits. Sinauer Associates, Inc., Sunderland, Massachusetts.
Burnham, K. P. and D. R. Anderson. 2002. Model selection and multimodel inference: a practical information-theoretic approach. Springer Science & Business Media.
coleoguy/SAGA2 documentation built on Feb. 2, 2023, 2:15 p.m.
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| SAGA-package | R Documentation |
Software for the Analysis of Genetic Architecture SAGA
Description
Provides multimodel parameter estimates of genetic architecture incorporating the impact of environmental and sex effects. Uses weighted least squares to solve all possible models. Chooses the model or models that are most appropriate based on AICc weights and produces model weighted parameter estimates and unconditional standard errors. A function to visualize the distribution of model fits across model space allows users to graphically see how well defined the "true" model is with their dataset.
Details
| Package: | SAGA |
| Version: | 2.0.0 |
Author(s)
Heath Blackmon
Jeffery P. Demuth
Andrew Armstrong
Jonathon Leach
Correspondence: Heath Blackmon at coleoguy@gmail.com
References
Blackmon, H. and J. Demuth. 2016. An information-theoretic approach to estimating the composite genetic effects contributing to variation among generation means: Moving beyond the joint-scaling test for line cross analysis. Evolution 70:2 420-432.
Lynch, M. and B. Walsh. 1998. Genetics and analysis of quantitative traits. Sinauer Associates, Inc., Sunderland, Massachusetts.
Burnham, K. P. and D. R. Anderson. 2002. Model selection and multimodel inference: a practical information-theoretic approach. Springer Science & Business Media.
R Package Documentation
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