BAMMtools (version 2.1.12)

samplingProbs: Creates clade-specific sampling fractions

Description

If the user would like to specify species sampling on a clade-by-clade basis, a sampling probability table can be provided to BAMM.

Usage

samplingProbs(
  tree,
  cladeTable,
  cladeRichness = NULL,
  globalSampling,
  output,
  writeToDisk = TRUE
)

Value

If writeToDisk = TRUE, then no object is returned. If

writeToDisk = FALSE, then a dataframe is returned. The resultant table must contain one row for each species in the phylogeny, along with clade assignment, and sampling fraction. The first line must contain the overall sampling fraction for the phylogeny and must be written as tab-delimited, with no headers.

Arguments

tree

An object of class phylo.

cladeTable

A dataframe with one column of species names and a second column of group assignment.

cladeRichness

Either NULL or a vector of species counts, named by clade names.

globalSampling

percent sampling for the backbone of the phylogeny.

output

Path + output file name.

writeToDisk

A logical, should the table be written to disk, defaults to TRUE.

Author

Pascal Title

Details

This function handles two types of input: The cladeTable can either contain the species found in the phylogeny, along with clade assignment of those species, or it can contain more species than found in the phylogeny. If the table only contains those species in the phylogeny, then a vector cladeRichness must be provided that contains known clade richness. If the cladeTable contains more than the species in the phylogeny, then cladeRichness should be set to NULL. The globalSampling value should represent the overall completeness of species sampling in terms of major clades. See http://bamm-project.org/ for additional details.

Examples

# Generate dummy data
tree <- read.tree(text="(((t1:2,(t2:1,t3:1):1):1,((t4:1,t5:1):1,t6:2):1)
                  :1,(t7:3,(t8:2,t9:2):1):1);")
tree$tip.label <- paste(rep('Species',9),1:9,sep='')

spTable <- as.data.frame(matrix(nrow=9,ncol=2))
spTable[,1] <- tree$tip.label
spTable[1:3,2] <- 'cladeA'
spTable[4:6,2] <- 'cladeB'
spTable[7:9,2] <- 'cladeC'
richnessVec <- c(cladeA=5, cladeB=4, cladeC=12)

# Option 1: We have a table of clade assignment for the species in the
#           tree, along with a vector of known clade richness
spTable
richnessVec
samplingProbs(tree, cladeTable = spTable, cladeRichness = richnessVec,
              globalSampling = 1, writeToDisk = FALSE)

# Option 2: We have a table of known species, beyond the sampling in the
#           phylogeny
spTable <- rbind(spTable, c('Species10','cladeA'),c('Species11','cladeA'),
                 c('Species12','cladeC'), c('Species13','cladeC'),
                 c('Species14','cladeC'))

spTable

samplingProbs(tree, cladeTable = spTable, cladeRichness = NULL, 
              globalSampling = 0.9, writeToDisk = FALSE)