modelAnalysis: Model Analysis

Description

A collection of functions to summarize the topological properties of trees generated by a model or other large groups of trees. Note that the examples section is quite detailed and many be useful to read. Some of them use the library ggplot2 which will need to be installed seperately. More examples like this can be found in exampleSummary.

Usage

modelSummary(model,topList,n,loadingBar=TRUE)
modelCreate(model,n,loadingBar=FALSE)
treeListSummary(treeList,topList,loadingBar=TRUE)

Arguments

model

A function with no inputs which results in an object of class phylo4. For example, model = function() {rtree(50)} is allowed. It is intended that there be some randomness in the model.

topList

This is a list of functions. Each function in the list acts on an object of class phylo4 and returns a number - which is intended to be a topological property of the tree.

An integer specifying the number of trees create.

loadingBar

A Boolean. If true, it results in the number trees evaluated so far being printed as each tree is evaluated. This gives some indication of how long the function will take.

treeList

A list where the elements of the list are cobjects of class phylo4.

Value

modelSummaryA data frame with the rows corresponding to the different trees and the columns to the result of the topological summaries in topList
modelCreateA list of n objects of class phylo4
treeListSummaryA data frame with the rows corresponding to the different trees and the columns to the result of the topological summaries in topList

Details

modelSummary generates some trees using the model created and returns a data frame with the topological summaries in topList. The rows correspond to the trees generated and the columns to the different summaries. modelCreate creates a list of n trees using the model specified. Note that loadingbar is FALSE by default here as most model's are likely to be quick. treeListSummary performs the functions in topList on a list of trees. It returns a data frame where the rows correspond to the trees and the columns to the different summaries.

Examples

Run this code

## Create a function to test
maxLadd <- function(tree) {max(ladderNums(tree))}

## Create a model 
## NOTE THE USE OF 'as'
bdModel <- function() {as(rlineage(0.5,0.1,Tmax=6),'phylo4')}

## A simple example of modelSummary
modelSummary(function(){rtree4(50)},topList=c(avgLadder,sackin),n=10)

## Another example using the created model and function
modelSummary(bdModel,topList=c(maxLadd,sackin),n=10,loadingBar=FALSE)

## Create a list of trees for more detailed analysis
bdTrees <- modelCreate(bdModel,10)

## Select a number of topological summaries to calculate
summaries <- c(avgLadder,maxLadd,sackin,function(tree){nConfig(tree,2)})

## Calculate these summaries for the trees in question
bdSummary <- treeListSummary(bdTrees,summaries)
names(bdSummary) <- c('avgLadder','maxLadd','Sackin','Cherries')

### I WILL NOW ILLUSTRATE SOME USES OF THE PACKAGE
### THESE METHODS USE LIBARY ggplot2
### IT WILL NEED TO BE SEPERATELY INSTALLED IF YOU WISH TO USE IT

# Initialize a plot
p <- ggplot(data=bdSummary)

# Simple bar plot
p + geom_bar(aes(avgLadder),binwidth=0.1)

# Simple scatter plot
p + geom_point(aes(x=Sackin,y=Cherries))

### See exampleSummary for more examples of plots