Difference between revisions of "R Hackathon 1/PGLS"

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PGLS is a powerful method for analyzing continuous data that has been applied to estimating adaptive optima (Butler and King 2004) and estimating the relationships among traits (e.g., body size and geographic range size in carnivores).  PGLS allows the user to specify different ways in which the tree structure is expected to affect the covariance in trait values across taxa.  For example, the user might assume that the trait evolves by Brownian motion and thus that the trait covariance between any pair of taxa decreases linearly with the time (in branch length) since their divergence.  Alternately, the user might apply a Ornstein-Uhlenbeck model where the expected covariance decreases exponentially, as governed by the parameter alpha (Martins and Hansen 1997).    These methods are implemented in the ape package.
 
PGLS is a powerful method for analyzing continuous data that has been applied to estimating adaptive optima (Butler and King 2004) and estimating the relationships among traits (e.g., body size and geographic range size in carnivores).  PGLS allows the user to specify different ways in which the tree structure is expected to affect the covariance in trait values across taxa.  For example, the user might assume that the trait evolves by Brownian motion and thus that the trait covariance between any pair of taxa decreases linearly with the time (in branch length) since their divergence.  Alternately, the user might apply a Ornstein-Uhlenbeck model where the expected covariance decreases exponentially, as governed by the parameter alpha (Martins and Hansen 1997).    These methods are implemented in the ape package.
  
Let's return to the Geospiza dataset (within the geiger package) to try PGLS.  We assume that you have already loaded the necessary packages (geiger for the data and ape for the function) as described on [https://www.nescent.org/wg_phyloinformatics/R_Hackathon/TransitionProbability this page]. We will first build the correlation structure expected if the traits evolve by Brownian motion.
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Let's return to the ''Geospiza'' dataset (within the geiger package) to try PGLS.  We assume that you have already loaded the necessary packages (geiger for the data and ape for the function) as described on [https://www.nescent.org/wg_phyloinformatics/R_Hackathon/TransitionProbability this page]. Let's say we want to test whether there is a significant relationship between wing length and tarsus length, accounting for possible dependence among the data points (trait values) due to phylogenetic relatedness.
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First, we will create a data frame containing our traits of interest, with the row.names matching the tip.labels.  NOTE:
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We will first build the correlation structure expected if the traits evolve by Brownian motion.
  
 
library(geiger)
 
library(geiger)

Revision as of 13:47, 13 December 2007

Phylogenetic Generalized Least Squares

PGLS is a powerful method for analyzing continuous data that has been applied to estimating adaptive optima (Butler and King 2004) and estimating the relationships among traits (e.g., body size and geographic range size in carnivores). PGLS allows the user to specify different ways in which the tree structure is expected to affect the covariance in trait values across taxa. For example, the user might assume that the trait evolves by Brownian motion and thus that the trait covariance between any pair of taxa decreases linearly with the time (in branch length) since their divergence. Alternately, the user might apply a Ornstein-Uhlenbeck model where the expected covariance decreases exponentially, as governed by the parameter alpha (Martins and Hansen 1997). These methods are implemented in the ape package.

Let's return to the Geospiza dataset (within the geiger package) to try PGLS. We assume that you have already loaded the necessary packages (geiger for the data and ape for the function) as described on this page. Let's say we want to test whether there is a significant relationship between wing length and tarsus length, accounting for possible dependence among the data points (trait values) due to phylogenetic relatedness.

First, we will create a data frame containing our traits of interest, with the row.names matching the tip.labels. NOTE:


We will first build the correlation structure expected if the traits evolve by Brownian motion.

library(geiger)