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by: Yazmin Schmidt


Yazmin Schmidt
Texas A&M
GPA 3.66


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About this Document

Class Notes
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Popular in Genetics (Graduate Group)

This 14 page Class Notes was uploaded by Yazmin Schmidt on Wednesday October 21, 2015. The Class Notes belongs to GENE 606 at Texas A&M University taught by Staff in Fall. Since its upload, it has received 19 views. For similar materials see /class/225934/gene-606-texas-a-m-university in Genetics (Graduate Group) at Texas A&M University.




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Date Created: 10/21/15
Model Selection GENE 606ENTO 606 Review of models Reversible GTR family rate matrix amongsite rate variation base frequencies Nonreversible accommodate base frequency changes in different parts of tree accommodate changes in rates of sites in different parts of tree Nonindependence of sites Codon models To cover in AminoAcid Analyses Lecture rRNA models Partitioned models To cover in Data Partitions Lecture Assume different models for each partition eg different genes codons stem vs loop Why and when is the model important VVHgtltgt Not Critical m Figure 2 The effect of topology on robustness At the center of the continuum phylogenetics signal is strong and model choice is not critical ie maximum likelihood is robust to Violations of model assumptions In the Felsenstein zone left model selection is critical as is also the case for the inverse Felsenstein zone right From Sullivan amp Joyce 2005 Model Selection All models are wrong but some are useful Box 1976 Model selection is a way of approximating not identifying full reality Statistical model selection is based on the parsimony principle hypotheses should be kept as simple as possible Increasing the number of parameters will increase the fit between the model and the data increase the likelihood but at a cost Tradeoff between bias and variance Bias distance between the average estimate and truth Variance spread of the estimates around the truth Bias2 Bias vs Variance Variance Few Many Number of parameters From Posada amp Buckley 2004 Model Selection Strategies Likelihood Ratio Tests Modeltest Akaike Information Criterion Modeltest Bayesian Information Criterion Modeltest PerformanceBased model selection DT ModSel Likelihood Ratio Test LRT 5 2lnL1 lnL0 Can only be used to evaluate nested hypotheses L1 more complex model Test statistic evaluated under assumption of asymptotic convergence to X2 df diff in parameters Hierarchical hLRT in phylogenetics 1 lnfer a phylogenetic tree with another method parsimony or distance 2 Estimate the likelihood of that tree under different models of the GTR family 56 models in Modeltest program 3 Conduct LRT in a hierarchical fashion 1 m m A A p we m nm km m m x wahm Potential weaknessess of hLRT Dependence on initial estimate of topology Use of initial trees has little effect on model chosenbut Very poor trees can yield very poor model estimates Arbitrary order of comparison can have effect on which model is selected Can only compare nested hypotheses Akaike Information Criteria AIC AIC 2 In L 2k Fora particular model i L max log likelihood k number of parameters prefer the model with the smallest AIC provides a measure of fit between model and data and includes a penalty for overparameterization Small sample sizes 11 k lt 40 use AICC Bayesian Information Criteria BIC BIC 21nLi k 111 n For a particular model i L max log likelihood k number of parameters n sample size of characters provides a measure of fit between model and data and penalizes for overparameterization more heavily than AIC especially with large n Comparison of model selection approaches hLRT AIC and BIC all use an initial topology hLRT can only compare nested hypotheses while AIC and BIC can compare multiple nested and non nested hypotheses AIC and BIC outcome does not depend on the order of comparisons while hLRT does AIC and BIC allow assessment model selection uncertainty and estimation of phylogenies and model parameters using all available models model averaged inference or multimodel inference Implementation of model selection approaches Modeltest 37 httpdanvinuviqoessotharemodeltesthtml Modeltest 38 WebServer httpdanNinuviqoes A command file for PAUP is already available within the Modeltest package lnfers a NJ tree Estimates likelihoods and parameter estimates for each of 56 models output modelscores Use modelscores file as input for Modeltest


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