evobiR: Evolutionary Biology in R

Description

evobiR is a collection of tools for use in evolutionary biology. Some of the functions manipulate data in a way not implemented by other functions while others calculate sequence statistics or perform simulations, either of data across trees or genetic and genomic simulations.

Details

More information on evobiR is available at http://coleoguy.github.io/software.html

Author(s)

Heath Blackmon

Maintainer: Heath Blackmon <coleoguy@gmail.com>

simulated SNP data

Description

This file contains simulated SNP data

Author(s)

Heath Blackmon

References

http://coleoguy.github.io/

Computes an AICc score

Description

Supplied with a log likelihood, the number of model parameters, and sample size calculates the small sample size version of the AIC score.

Usage

AICc(loglik, K, N)

Arguments

Details

Returns an AICc score.

Author(s)

Heath Blackmon

References

http://coleoguy.github.io/

Examples

AICc(-32, 3, 100)

Calculate the mean of a continuous character at the origin of derived state of a binary charachter

Description

This function uses stochastic mapping and ancestral state reconstruction to determine if the derived state of a binary trait originates when a continuous trait has an extreme value.

Usage

AncCond(trees, data, derived.state, iterations=1000)

Arguments

Details

This function uses stochastic mapping and ancestral state reconstruction as implemented in phytools to determine if the derived state of a binary trait originates when a continuous trait has an extreme value. This test assumes that the derived state of the binary character may lead to correlated selection in the continuous trait. Because of this the ancestral state reconstruction of the continuous trait is based only on data from species that remain in the ancestral condition for the binary trait

Value

Returns a plot of the null distribution and the observed data as well as empirical p-value for the observed data.

Author(s)

Heath Blackmon and Richard H. Adams

References

http://coleoguy.github.io/

Examples

## Not run: 
data(mite.trait)
data(trees.mite)
AncCond(trees, mite.trait, derived.state = "haplodiploidy", iterations=100) 

## End(Not run)

Calculate Patterson's D-statistic

Description

These functions calculate Patterson's D-statistic to compare the frequencies of discordant SNP genealogies. These tests assume equal substitution rates and unlinked loci, D-statistics significantly different from 0 suggest that introgression has occurred.

Usage

CalcD(alignment = "alignment.fasta", sig.test = "N", block.size = 1000, replicate = 1000)

CalcPopD(alignment = "alignment.fasta")

Arguments

Details

The functions CalcD and CalcPopD are implementations of the algorithm described in Durand et al. 2011. Significance of the D-stat can be calculated either through bootstrapping or jackknifing. Bootstrapping is appropriate for datasets where SNPs are unlinked for instance unmapped RADSeq data. Jackknifing is the appropriate approach when SNPs are potentially in linkage for instance gene alignments or genome alignments.

Value

Returns the number of each type of site, Z scores and p-values

Author(s)

Heath Blackmon

References

http://coleoguy.github.io/

Durand, Eric Y., et al. Testing for ancient admixture between closely related populations. Molecular biology and evolution 28.8 (2011): 2239-2252.

Eaton, D. A. R., and R. H. Ree. 2013. Inferring phylogeny and introgression using RADseq data: An example from flowering plants (Pedicularis: Orobanchaceae). Syst. Biol. 62:689-706

Examples

CalcD(alignment = system.file("1.fasta", package = "evobiR"), sig.test = "N")

CalcPopD(alignment = system.file("3.fasta", package = "evobiR"))
  

Tests whether a number is even

Description

Just a simple function that returns True if a number is even and False otherwise.

Usage

Even(x)

Arguments

Details

Returns a vector of logical values of the same length as the input vector. If the input value is not a number it will return an error message.

Author(s)

Heath Blackmon

References

http://coleoguy.github.io/

Examples

Even(c(1,2,3,4,5,6,2,5))

Find Close Matches in a tree and dataset

Description

When assembling data from different sources typos can sometimes cause a loss of perfect matches between trees and datasets. This function helps you find these close matches that can be hand curated to keep as many species as possible in your analysis.

Usage

FuzzyMatch(tree, data, max.dist)

Arguments

Value

A dataframe with the following rows:

Name in data
Name in tree
Number of differences

Author(s)

Heath Blackmon

References

http://coleoguy.github.io/

Examples

data(hym.tree)
names <- c("Pepsis_elegans", "Plagiolepis_alluaudi", "Pheidele_lucreti",
           "Meliturgula_scriptifronsi", "Andrena_afimbriat")
FuzzyMatch(tree = hym.tree, data = names, max.dist=3)

Calcualtes the mode of a numeric vector

Description

R's base package function mode returns the type of object 'numeric', 'character' etc. This give the option of an easy to remember work around for that.

Usage

Mode(x)

Arguments

Details

Returns the most frequently occuring value in a vector. In the case of a tie it will return the mode which has the earliest initial occurence in the vector

Value

returns the most frequently occuring value in a series of numbers

Author(s)

Heath Blackmon

References

http://coleoguy.github.io/

Examples

Mode(c(1,2,3,4,5,6,2,5))

Create Simulated Datasets via PPS

Description

This function performs posterior predictive simulations of discrete traits. The function is written to work with the output of bayesian programs that produce a collection of rate matrix parameter estimates based on either one or a collection of trees.

Usage

PPSDiscrete(trees, MCMC, states, N = 2)

Arguments

Value

A matrix is returned with the rownames being the species names from the tree and each column containing a result of a single PPS.

Author(s)

Heath Blackmon

References

http://coleoguy.github.io/

Examples

data(trees)
data(mcmc2)
data(mcmc3)
# 1 tree 100 q-mats 3 states
PPSDiscrete(trees[[1]], MCMC=mcmc3[,2:10], states=c(.5,.2,.3), N=2)
# 10 trees 100 q-mats 3 states
PPSDiscrete(trees, MCMC=mcmc3, states=c(.5,.2,.3), N=10)
# 10 trees 100 q-mats 2 states
PPSDiscrete(trees, MCMC=mcmc2, states=c(.5,.5), N=10)

Reorders trait data to match the order of tips in a tree

Description

This function takes a vector, matrix, or dataframe and reorders the data to match the order of tips in a phylo object.

Usage

ReorderData(tree, data, taxa.names="row names")

Arguments

Details

Returns data in the same format as supplied but reordered to match the order of tip labels.

Author(s)

Heath Blackmon

References

http://coleoguy.github.io/

Selection on Residuals

Description

This function takes measurements of multiple traits and performs a linear regression and identifies those records with the largest and smallest residual. Originally it was written to perform a regression of horn size on body size allowing for high and low selection lines.

Usage

ResSel(data, traits, percent = 10, identifier = 1, model = "linear")

Arguments

Value

This function returns a list

Author(s)

Heath Blackmon

References

http://coleoguy.github.io/

Examples

data <- read.csv(file = system.file("horn.beetle.csv", package = "evobiR"))
ResSel(data = data, traits = c(2,3), percent = 15, identifier = 1, model = "linear")

Select a random sample of trees

Description

This function takes as its input a large collection of trees from a program like MrBayes or Beast and allows the user to select the number of randomly drawn trees they wish to retrieve

Usage

SampleTrees(trees, burnin, final.number, format, prefix)

Arguments

Value

an object of the class "multiPhylo" is returned

Author(s)

Heath Blackmon

References

http://coleoguy.github.io/

Examples

SampleTrees(trees = system.file("trees.nex", package = "evobiR"), 
            burnin = .1, final.number = 20, format = 'new', prefix = 'sample')

Sliding window analysis

Description

Applies a function within a sliding window of a numeric vector. Both the step size and the window size can be set by the user.

Usage

SlidingWindow(FUN, data, window, step)

Arguments

Details

Returns a vector of numeric values representing the applying the selected function within each window. The length will be unequal to the original data and will be determined primarily by the step size.

Author(s)

Heath Blackmon

References

http://coleoguy.github.io/

Examples

data <- c(1,2,1,2,10,2,1,2,1,2,3,4,5,6,2,5)
SlidingWindow("mean", data, 3, 1)

creates a supermatrix from multiple gene alignments

Description

combines all alignments in a folder into a single supermatrix

Usage

SuperMatrix(missing = "-", prefix = "concatenated", save = T)

Arguments

Details

This function reads all fasta format alignments in the working directory and constructs a single supermatrix that includes all taxa present in any of the fasta files and inserts missing symbols for taxa that are missing sequences for some loci.

Value

A list with two elements is returned. The first element contains partition data that records the alignment positions of each input fasta file in the combined supermatrix. The second element is a dataframe version of the supermatrix. If the argument save is set to True then both of these files are also saved to the working directory.

Author(s)

Heath Blackmon

References

http://coleoguy.github.io/

Examples

## Not run: 
SuperMatrix(missing = "N", prefix = "DATASET2", save = T)

## End(Not run)

Learning Resources

Description

This uses the shiny app to produce interactive pages.

Usage

ViewEvo(simulation)

Arguments

Details

The wf.model was implemented to illustrate to students the effects of genetic drift. In particular the high likelihood of losing a beneficial allele when population size is finite. The bd.model will plot 4 phylogenetic trees based on a birth death model with a single set of parameters. This application was developed to illustrate the high variability of a birth death process as a generating model for phylogenies and the inherint difficulty in detecting differential diversification rates. Finally the dist.model was developed to help illustrate the relationship between common statistical distributions often used as priors and the way that parameters effect the density distribution.

Value

This function returns an interactive webpage.

Author(s)

Heath Blackmon

References

http://coleoguy.github.io/

Wright-Fisher Simulator: https://evobir.shinyapps.io/wf_model/

Birth-death Simulator: https://evobir.shinyapps.io/bd_model

Statistical Distribution: https://evobir.shinyapps.io/dist_model

Examples

## Not run: 
ViewEvo("wf.model")
ViewEvo("bd.model")
ViewEvo("dist.model")

## End(Not run)

Calculate Patterson's D-statistic in sliding windows

Description

This functions calculate Patterson's D-statistic in windows.

Usage

WinCalcD(alignment = "alignment.fasta", win.size = 100, step.size=50,
boot = F, replicate = 1000)

Arguments

Details

This function is just an extension of CalcD and calculates D statistic for windows.

Value

Returns a table with the number of each type of site, Z scores and p-values for each window in the genome

Author(s)

Heath Blackmon

References

http://coleoguy.github.io/

Durand, Eric Y., et al. Testing for ancient admixture between closely related populations. Molecular biology and evolution 28.8 (2011): 2239-2252.

Eaton, D. A. R., and R. H. Ree. 2013. Inferring phylogeny and introgression using RADseq data: An example from flowering plants (Pedicularis: Orobanchaceae). Syst. Biol. 62:689-706

Examples

WinCalcD(alignment = system.file("1.fasta", package = "evobiR"), 
         win.size=100, step.size=50, boot = TRUE, replicate=10)
  

Gnatocerus measurements

Description

A csv file containing measurements of horn and body size for the beetle Gnatocerus cornutus.

Phylogenetic tree

Description

This is a phylogenetic tree with 5 species of hymenoptera.

mcmc log file

Description

an mcmc log file. The first column is the tree used during the iteration the remaining columns are the rate parameters of the Q matrix listed by column order.

phenotype data for mites

Description

dataframe of sexual system and chromosome number data for mites

10 Phylogenetic trees

Description

This is a collection of 10 simulated phylogenetic trees with 200 tips each.

10 Phylogenetic trees

Description

These are trees from a previously published work on mite sexual system evolution.

100 Phylogenetic trees

Description

This is a collection of 100 simulated phylogenetic trees with 10 tips each.