Uncertainty in tips with binary attributes
Source:vignettes/articles/uncertainty_binary.Rmd
uncertainty_binary.Rmd
library(ape)
library(phytools)
#> Loading required package: maps
library(dplyr)
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library(tidyr)A few attributes in bugphyzz only have ‘TRUE’ annotations. Other attributes have both ‘TRUE’ and ‘FALSE’ annotations.
Tips that are uncertain could be treated as FALSE annotations (imputed data) or uncertain annotations with prior probabilities set to 0.5 for TRUE and 0.5 for FALSE. In any case, ASR is not very reliable with any of these approaches, especially when the percentage of tips with annotations is low.
data('primate.tree')
data('primate.data')
tree <- primate.tree
data <- primate.data
data <- data[tree$tip.label,]
# rownames(data) <- paste0('taxon', 1:nrow(data))
# tree$tip.label <- paste0('taxon', 1:Ntip(tree))
myMod <- 'ARD' # 'ARD'
myPi <- 'fitzjohn'
original <- data |>
tibble::rownames_to_column(var = 'Taxa') |>
select(Taxa, Activity_pattern) |>
mutate(Presence = 1) |>
pivot_wider(
names_from = 'Activity_pattern', values_from = 'Presence',
values_fill = 0
) |>
arrange(Taxa) |>
tibble::column_to_rownames(var = 'Taxa') |>
select(Diurnal) |>
mutate(
not_diurnal = ifelse(Diurnal == 0, 1, 0)
) |>
as.matrix()
colnames(original) <- c('A--TRUE', 'A--FALSE')
head(sort(table(sub('^(\\w+)_.*$', '\\1', rownames(original))), decreasing = TRUE))
#>
#> Eulemur Galago Ateles Callithrix Cercopithecus
#> 4 4 3 3 3
#> Macaca
#> 3Original
fit <- fitMk(tree = tree, x = original,
model = myMod, pi = myPi,
lik.func = "pruning", logscale = TRUE)
ace <- ancr(fit, tips = TRUE)
plot(ace, args.plotTree = list(direction = "upwards"))
title(main = '0% uncertain tips', line = -1)
m1 <- original
m1[which(grepl('^Macaca_', rownames(m1))), 1] <- 1
m1[which(!grepl('^Macaca_', rownames(m1))), 1] <- 0
m1[,2] <- ifelse(m1[,1] == 1, 0, 1)
fit1 <- fitMk(tree = tree, x = m1,
model = myMod, pi = myPi,
lik.func = "pruning", logscale = TRUE)
ace1 <- ancr(fit1, tips = TRUE)
plot(ace1, args.plotTree = list(direction = "upwards"))
title(main = 'Macaca TRUE', line = -1, sub = '0/1 T/F for unknowns')
m2 <- original
m2[] <- 0.5
m2[which(grepl('^Macaca_', rownames(m2))), 1] <- 1
m2[,2] <- ifelse(m2[,1] == 1, 0, 0.5)
fit2 <- fitMk(tree = tree, x = m2,
model = myMod, pi = myPi,
lik.func = "pruning", logscale = TRUE)
ace2 <- ancr(fit2, tips = TRUE)
plot(ace2, args.plotTree = list(direction = "upwards"))
title(main = 'Macaca TRUE', line = -1, sub = '0.5 T/F for unknowns')
m3 <- original
m3[] <- 0.5
m3[which(grepl('^Macaca_', rownames(m3))), 1] <- 1
m3[which(grepl('^Macaca_', rownames(m3))), 2] <- 0
m3[which(grepl('^Galago_', rownames(m3))), 2] <- 1
m3[which(grepl('^Galago', rownames(m3))), 1] <- 0
fit3 <- fitMk(tree = tree, x = m3,
model = myMod, pi = myPi,
lik.func = "pruning", logscale = TRUE)
ace3 <- ancr(fit3, tips = TRUE)
plot(ace3, args.plotTree = list(direction = "upwards"))
title(main = 'Macaca TRUE - Galago FALSE', line = -1, sub = '0.5 T/F for unknowns')
Session information
sessioninfo::session_info()
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