Phylogenetic signal in missing data

Calculates D statistic (Fritz & Purvis 2010), a measure of phylogenetic signal, for missing data. Missingness is recoded into a binary variable (1=missing, 0=non missing). This function is an adaptation of phylo.d for missing data.

miss.phylo.d(data, phy, ...)

Arguments

data	Data frame containing species traits with species as row names.
phy	A phylogeny (class 'phylo', see ?ape).
...	Further arguments to be passed to phylo.d.

Value

The function miss.phylo.d returns an object of class "phylo.d" with the following components, for complete list of arguments see phylo.d :

DEstimate: The estimated D value

Pval1: A p value, giving the result of testing whether D is significantly different from one

Pval0: A p value, giving the result of testing whether D is significantly different from zero The function also prints the percentage of missing data per variable in the dataset.

Details

This function builds on phylo.d to calculate a phylogenetic signal in missing data. The variable of interest, usually a trait, is recoded into a binary variable (1=missing data, 0=non missing data). Then the phylo.d function tests the estimated D value for significant departure from both random association and the clumping expected under a Brownian evolution threshold model (Fritz & Purvis, 2010).

Output can be visualised using print() and plot()

References

Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467

Fritz, S. A. and Purvis, A. (2010). Selectivity in mammalian extinction risk and threat types: a new measure of phylogenetic signal strength in binary traits. Conservation Biology, 24(4):1042-1051.

David Orme, Rob Freckleton, Gavin Thomas, Thomas Petzoldt, Susanne Fritz, Nick Isaac and Will Pearse (2013). caper: Comparative Analyses of Phylogenetics and Evolution in R. R package version 0.5.2. https://CRAN.R-project.org/package=caper

Examples

# Load caper:
library(caper)
#> Loading required package: MASS
#> Loading required package: mvtnorm
# Load data
data(primates)
data<-alien$data
phy=alien$phy[[1]]

# Test phylogenetic signal for missing data:
sexNAsig <- miss.phylo.d(data,phy,binvar=homeRange)
#> [1] "Percentage of missing data in traits:"
#>    family adultMass  gestaLen homeRange   SD_mass  SD_gesta  SD_range 
#>      0.00      2.13      9.57     44.68      2.13      9.57     44.68 
print(sexNAsig)
#> 
#> Calculation of D statistic for the phylogenetic structure of a binary variable
#> 
#>   Data :  data
#>   Binary variable :  homeRange
#>   Counts of states:  0 = 52
#>                      1 = 42
#>   Phylogeny :  phy
#>   Number of permutations :  1000
#> 
#> Estimated D :  0.9470363
#> Probability of E(D) resulting from no (random) phylogenetic structure :  0.35
#> Probability of E(D) resulting from Brownian phylogenetic structure    :  0
#> 
plot(sexNAsig)

massNAsig <- miss.phylo.d(data,phy,binvar=adultMass)
#> [1] "Percentage of missing data in traits:"
#>    family adultMass  gestaLen homeRange   SD_mass  SD_gesta  SD_range 
#>      0.00      2.13      9.57     44.68      2.13      9.57     44.68 
print(massNAsig)
#> 
#> Calculation of D statistic for the phylogenetic structure of a binary variable
#> 
#>   Data :  data
#>   Binary variable :  adultMass
#>   Counts of states:  0 = 92
#>                      1 = 2
#>   Phylogeny :  phy
#>   Number of permutations :  1000
#> 
#> Estimated D :  0.9014547
#> Probability of E(D) resulting from no (random) phylogenetic structure :  0.385
#> Probability of E(D) resulting from Brownian phylogenetic structure    :  0.266
#> 
plot(massNAsig)