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-# Character evolution
+# Independent contrasts
-In these examples you will explore the evolution of characters on phylogenetic 
-trees. This is a bit different than our previous analyses, where we started 
-with character data and built a tree. In the present analyses, we will start 
-with both a tree and character data. In most situations the tree will have been 
-inferred from molecular sequence data, and the character data will be 
-In the previous exercises where we built trees, you used a variety of programs 
-at the command line. These programs were there themselves written in several 
-different languages. In the past decade, a large community of phylogenetic 
-biologists interested in character evolution have written many analysis tools 
-in the programming language R. This set of tools provides a rich environment 
-for the study of character evolution. All of our character evolution analyses 
-will be conducted as interactive R sessions. 
-## Getting oriented with R
-R, like python and many other languages, can be used interactively, where you 
-enter a few commands at a time, or in batch mode, where a series of commands are 
-placed in a file and executed all at once. We will use R interactively, and 
-load R code that others have already written for phylogenetic analyses as we 
-There are already *many* general introductions, tutorials, and quick-references 
-for R. I therefore won't provide a background on R itself, we will dive right 
-into some analyses.
-There is detailed information on phylogenetic analysis with R at both 
-(the R phylo wiki)[] and the 
-(CRAN task page for phylogenetics)[].
-For the analyses below, you will need to install the `ape` library in R.
-## Phylogenetic Independent Contrast 
-Copy the data files (`Geospiza.txt` and `Geospiza.nex`) to a directory on your 
-computer. Open the R interface (which was installed alongside the rest of the 
-R components) or open a terminal window and launch an interactive R session 
-with the command `R`.
-Now, change to the directory where your data files are:
-    setwd('DATA_DIR')
-Where `DATA_DIR` is the directory where you put the data files. The R command 
-`setwd()` is much like the shell command `cd` - it controls your working 
-Load and prepare the data:
-    geodata<-read.table("Geospiza.txt")
-    geotree<"Geospiza.nex")
-    geotree <- drop.tip(geotree, "olivacea")
-That last command is necessary since there is one taxon in the tree that isn't 
-in the data file.
+See `independent_contrasts.pdf`.


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