1,721,015 research outputs found
(Revised) Hypothesis Tests and Confidence Intervals Involving Fitness Landscapes fit by Aster Models
No full text1 online resource (PDF, 10 pages)Geyer, Charles J.; Shaw, Ruth G.. (2010). (Revised) Hypothesis Tests and Confidence Intervals Involving Fitness Landscapes fit by Aster Models. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/199683
Aster Models with Random Effects via Penalized Likelihood
Get PDF1 online resource (PDF, 61 pages)Geyer, Charles J.; Ridley, Caroline E.; Latta, Robert G.; Etterson, Julie R.; Shaw, Ruth G.. (2010). Aster Models with Random Effects via Penalized Likelihood. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/199694
Aster Models with Random Effects and Additive Genetic Variance for Fitness
Get PDFThis technical report is a minor supplement to the paper Geyer et al. (in press) and its accompanying technical report Geyer et al. (2012). It shows how to move variance components from the canonical parameter scale to the mean value parameter scale. This is useful in estimating additive genetic variance for fitness, and that appears in Fisher's fundamental theorem of natural selection, which predicts the rate of increase in fitness via natural selection.Geyer, Charles J.; Shaw, Ruth G.. (2013). Aster Models with Random Effects and Additive Genetic Variance for Fitness. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/152355
Aster Models and Lande-Arnold Beta
No full textLande and Arnold (1983) proposed an estimate of beta, the directional selection gradient, by ordinary least squares (OLS). Aster
models (Geyer, Wagenius and Shaw, 2007; Shaw, Geyer, Wagenius, Hangelbroek, and Etterson, 2008) estimate exactly the same beta, so providing no improvement over the Lande-Arnold method in point estimation of this quantity. Aster models do provide correct confidence intervals, confidence regions, and hypothesis tests for beta; in contrast, such procedures derived from OLS are often invalid because the assumptions for OLS are grossly incorrect.Geyer, Charles J.; Shaw, Ruth G.. (2010). Aster Models and Lande-Arnold Beta. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/56329
Commentary on Lande-Arnold Analysis
Get PDFA solution to the problem of estimating fitness landscapes was proposed by Lande
and Arnold (1983). Another solution, which avoids problematic aspects of the
Lande-Arnold methodology, was proposed by Shaw, Geyer, Wagenius, Hangelbroek, and Etterson (2008). This technical report goes through Lande-Arnold theory in detail paying
careful attention to problematic aspects. The only completely new material is a theoretical analysis of when the best quadratic approximation to a fitness landscape, which is what the Lande-Arnold method estimates, is a good approximation to the actual fitness landscape.Geyer, Charles J.; Shaw, Ruth G.. (2008). Commentary on Lande-Arnold Analysis. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/56218
More Supporting Data Analysis for "Unifying Life History Analysis for Inference of Fitness and Population Growth"
Get PDF1 online resource (PDF, 54 pages)Geyer, Charles J.; Wagenius, Stuart; Shaw, Ruth G.; Hangelbroek, Helen H.; Etterson, Julie R.. (2007). More Supporting Data Analysis for "Unifying Life History Analysis for Inference of Fitness and Population Growth". Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/199671
Supporting Data Analysis for "Unifying Life History Analysis for Inference of Fitness and Population Growth"
Get PDF1 online resource (PDF, 89 pages)Geyer, Charles J.; Wagenius, Stuart; Shaw, Ruth G.; Hangelbroek, Helen H.; Etterson, Julie R.. (2007). Supporting Data Analysis for "Unifying Life History Analysis for Inference of Fitness and Population Growth". Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/199668
Aster Models and Lande-Arnold Beta (revised)
No full textLande and Arnold (1983) proposed an estimate of beta, the directional selection gradient,
by ordinary least squares (OLS). Aster models (Geyer, Wagenius and Shaw, 2007; Shaw, Geyer, Wagenius, Hangelbroek, and Etterson, 2008) estimate exactly the same beta, so providing no improvement over the Lande-Arnold method in point estimation of this quantity. Aster models do provide correct confidence intervals, confidence regions, and hypothesis tests for beta; in contrast, such procedures derived from OLS are often invalid because the assumptions for OLS are grossly incorrect.
This revision fixes a bug which made the figure incorrect in the original.Geyer, Charles J.; Shaw, Ruth G.. (2010). Aster Models and Lande-Arnold Beta (revised). Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/56394
Supporting Data Analysis for a talk to be given at Evolution 2008 University of Minnesota, June 20-24
Get PDFA solution to the problem of estimating fitness landscapes was proposed by Lande
and Arnold (1983). Another solution, which avoids problematic aspects of the
Lande-Arnold methodology, was proposed by Shaw, Geyer, Wagenius, Hangelbroek, and Etterson
(2008), who also provided an illustrative example. Here we provide another example using simulated data that are more suitable to aster analysis.
All analyses are done in R (R Development Core Team, 2008) using the aster contributed package described by Geyer et al. (2007) except for analyses in the style of
Lande and Arnold (1983), which use ordinary least squares regression. Furthermore, all
analyses are done using the Sweave function in R, so this entire technical report and all
of the analyses reported in it are completely reproducible by anyone who has R with the aster package installed and the R noweb file specifying the document.Geyer, Charles J.; Shaw, Ruth G.. (2008). Supporting Data Analysis for a talk to be given at Evolution 2008 University of Minnesota, June 20-24. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/56204
Hypothesis Tests and Confidence Intervals Involving Fitness Landscapes fit by Aster Models
No full textThis technical report explores some issues left open in Technical Reports 669 and 670 (Geyer and Shaw, 2008a,b): for fitness landscapes fit using an aster models, we propose hypothesis tests of whether the landscape has a maximum and confidence regions for the location of the maximum. All analyses are done in R (R Development Core Team, 2008) using the aster contributed package described by Geyer, Wagenius and Shaw (2007) and Shaw, Geyer, Wagenius, Hangelbroek, and Etterson (2008). Furthermore, all analyses are done using the Sweave function in R, so this entire technical report and all of the analyses reported in it are completely reproducible by anyone who has R with the aster package installed and the R noweb file specifying the document. The revision fixes one error in the confidence ellipsoids in Section 4 (a square root was forgotten so the regions in the original were too big).Geyer, Charles J.; Shaw, Ruth G.. (2010). Hypothesis Tests and Confidence Intervals Involving Fitness Landscapes fit by Aster Models. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/56328
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