Examples with a single annotation
Source:vignettes/articles/smaller_example.Rmd
smaller_example.RmdCreate input tree and matrix
In this case, assigng a probability of 1 to A–TRUE and 0 to A–FALSE for the t1 tip.
set.seed(1234)
randomTree <- rtree(7, rooted = TRUE)
mat <- matrix(
data = rep(0.5, Ntip(randomTree) * 2), ncol = 2, dimnames = list(
rownames = paste0('t', 1:Ntip(randomTree)), colnames = c('A--TRUE', 'A--FALSE')
)
)
annotated_tips <- c('t3', 't2')
for (i in seq_along(randomTree$tip.label)) {
if (randomTree$tip.label[i] %in% annotated_tips) {
mat[randomTree$tip.label[i], ] <- c(1, 0)
} else {
mat[randomTree$tip.label[i], ] <- c(0, 1)
}
}
mat
#> colnames
#> rownames A--TRUE A--FALSE
#> t1 0 1
#> t2 1 0
#> t3 1 0
#> t4 0 1
#> t5 0 1
#> t6 0 1
#> t7 0 1Function for plotting
plotT <- function(tree, ace, input_tips, model = '', pi = '') {
model <- dplyr::case_when(
model == 'ER' ~ 'equal rates',
model == 'ARD' ~ 'all rates different',
model == 'SYM' ~ 'symmetrical rates'
)
title <- paste0(
'Model transition rates: ', model,
'\nRoot prior: ', pi
)
df <- as.data.frame(ace$ace)
knownNode <- which(rownames(df) %in% input_tips)
df$nodetip <- as.character(1:nrow(df))
pieList <- vector('list', nrow(df))
for (i in seq_along(pieList)) {
names(pieList)[i] <- i
dat <- reshape2::melt(df[i,], id.vars = 'nodetip')
colnames(dat)[which(colnames(dat) == 'variable')] <- 'state'
pieList[[i]] <- ggplot(dat, aes(y = value, fill = state, x="")) +
geom_col() +
coord_polar("y", start=0) +
theme_void()
}
leg1 <- gtable_filter(ggplot_gtable(ggplot_build(pieList[[1]])), "guide-box")
pieList <- lapply(pieList, function(x) x + theme(legend.position = 'none'))
p <- ggtree(tree) +
geom_highlight(node = knownNode, fill = "steelblue", alpha = 0.5) +
geom_inset(insets = pieList, width = 0.1, height = 0.1) +
geom_tiplab(offset = 0.05) +
labs(title = title) +
annotation_custom(grob = leg1, xmin = 1, xmax = 2, ymin = 1, ymax = 1)
p
}Run ASR for different models and prior root probabilities
models <- c('ER', 'ARD', 'SYM')
pis <- c('fitzjohn', 'equal', 'estimated')
plotList <- vector('list', length(models) * length(pis))
n <- 1
for (i in seq_along(models)) {
for (j in seq_along(pis)) {
fit <- fitMk(tree = randomTree, x = mat, model = models[i], pi = pis[j])
ace <- ancr(fit, tips = TRUE)
plotList[[n]] <- plotT(
randomTree, ace = ace, input_tips = annotated_tips,
model = models[i], pi = pis[j]
)
n <- n + 1
}
}
#> Using pi estimated from the stationary distribution of Q assuming a flat prior.
#> pi =
#> A--TRUE A--FALSE
#> 0.5 0.5
#>
#> Using pi estimated from the stationary distribution of Q assuming a flat prior.
#> pi =
#> A--TRUE A--FALSE
#> 0.253468 0.746532
#>
#> Using pi estimated from the stationary distribution of Q assuming a flat prior.
#> pi =
#> A--TRUE A--FALSE
#> 0.5 0.5
Examle 2
annotated_tips <- c('t1', 't5')
for (i in seq_along(randomTree$tip.label)) {
if (randomTree$tip.label[i] %in% annotated_tips) {
mat[randomTree$tip.label[i], ] <- c(1, 0)
} else {
mat[randomTree$tip.label[i], ] <- c(0, 1)
}
}
models <- c('ER', 'ARD', 'SYM')
pis <- c('fitzjohn', 'equal', 'estimated')
plotList <- vector('list', length(models) * length(pis))
n <- 1
for (i in seq_along(models)) {
for (j in seq_along(pis)) {
fit <- fitMk(tree = randomTree, x = mat, model = models[i], pi = pis[j])
ace <- ancr(fit, tips = TRUE)
plotList[[n]] <- plotT(
randomTree, ace = ace, input_tips = annotated_tips,
model = models[i], pi = pis[j]
)
n <- n + 1
}
}
#> Using pi estimated from the stationary distribution of Q assuming a flat prior.
#> pi =
#> A--TRUE A--FALSE
#> 0.5 0.5
#>
#> Using pi estimated from the stationary distribution of Q assuming a flat prior.
#> pi =
#> A--TRUE A--FALSE
#> 0.285714 0.714286
#>
#> Using pi estimated from the stationary distribution of Q assuming a flat prior.
#> pi =
#> A--TRUE A--FALSE
#> 0.5 0.5
mergedPlot <- ggarrange(plotlist = plotList, ncol = 3, nrow = 3)
mergedPlot
mat <- matrix(
data = rep(0.5, Ntip(randomTree) * 2), ncol = 2, dimnames = list(
rownames = paste0('t', 1:Ntip(randomTree)), colnames = c('A--TRUE', 'A--FALSE')
)
)
annotated_tips <- c('t3', 't2')
for (i in seq_along(randomTree$tip.label)) {
if (randomTree$tip.label[i] %in% annotated_tips) {
mat[randomTree$tip.label[i], ] <- c(1, 0)
}
}
mat
#> colnames
#> rownames A--TRUE A--FALSE
#> t1 0.5 0.5
#> t2 1.0 0.0
#> t3 1.0 0.0
#> t4 0.5 0.5
#> t5 0.5 0.5
#> t6 0.5 0.5
#> t7 0.5 0.5
models <- c('ER', 'ARD', 'SYM')
pis <- c('fitzjohn', 'equal', 'estimated')
plotList <- vector('list', length(models) * length(pis))
n <- 1
for (i in seq_along(models)) {
for (j in seq_along(pis)) {
fit <- fitMk(tree = randomTree, x = mat, model = models[i], pi = pis[j])
ace <- ancr(fit, tips = TRUE)
plotList[[n]] <- plotT(
randomTree, ace = ace, input_tips = annotated_tips,
model = models[i], pi = pis[j]
)
n <- n + 1
}
}
#> Using pi estimated from the stationary distribution of Q assuming a flat prior.
#> pi =
#> A--TRUE A--FALSE
#> 0.999934 0.000066
#>
#> Using pi estimated from the stationary distribution of Q assuming a flat prior.
#> pi =
#> A--TRUE A--FALSE
#> 0.999934 0.000066
#>
#> Using pi estimated from the stationary distribution of Q assuming a flat prior.
#> pi =
#> A--TRUE A--FALSE
#> 0.999934 0.000066
mergedPlot <- ggarrange(plotlist = plotList, ncol = 3, nrow = 3)
mergedPlot
mat <- matrix(
data = rep(0.5, Ntip(randomTree) * 2), ncol = 2, dimnames = list(
rownames = paste0('t', 1:Ntip(randomTree)), colnames = c('A--TRUE', 'A--FALSE')
)
)
annotated_tips <- c('t1', 't5')
for (i in seq_along(randomTree$tip.label)) {
if (randomTree$tip.label[i] %in% annotated_tips) {
mat[randomTree$tip.label[i], ] <- c(1, 0)
}
}
mat
#> colnames
#> rownames A--TRUE A--FALSE
#> t1 1.0 0.0
#> t2 0.5 0.5
#> t3 0.5 0.5
#> t4 0.5 0.5
#> t5 1.0 0.0
#> t6 0.5 0.5
#> t7 0.5 0.5
models <- c('ER', 'ARD', 'SYM')
pis <- c('fitzjohn', 'equal', 'estimated')
plotList <- vector('list', length(models) * length(pis))
n <- 1
for (i in seq_along(models)) {
for (j in seq_along(pis)) {
fit <- fitMk(tree = randomTree, x = mat, model = models[i], pi = pis[j])
ace <- ancr(fit, tips = TRUE)
plotList[[n]] <- plotT(
randomTree, ace = ace, input_tips = annotated_tips,
model = models[i], pi = pis[j]
)
n <- n + 1
}
}
#> Using pi estimated from the stationary distribution of Q assuming a flat prior.
#> pi =
#> A--TRUE A--FALSE
#> 0.999934 0.000066
#>
#> Using pi estimated from the stationary distribution of Q assuming a flat prior.
#> pi =
#> A--TRUE A--FALSE
#> 0.999934 0.000066
#>
#> Using pi estimated from the stationary distribution of Q assuming a flat prior.
#> pi =
#> A--TRUE A--FALSE
#> 0.999934 0.000066
mergedPlot <- ggarrange(plotlist = plotList, ncol = 3, nrow = 3)
mergedPlot
Example 3
mat <- matrix(
data = rep(0.5, Ntip(randomTree) * 2), ncol = 2, dimnames = list(
rownames = paste0('t', 1:Ntip(randomTree)), colnames = c('A--TRUE', 'A--FALSE')
)
)
annotated_tips <- c('t1', 't5')
for (i in seq_along(randomTree$tip.label)) {
if (randomTree$tip.label[i] %in% annotated_tips) {
mat[randomTree$tip.label[i], ] <- c(1, 0)
} else {
mat[randomTree$tip.label[i], ] <- c(0, 1)
}
}
mat["t4",] <- c(0.5, 0.5)
mat["t7",] <- c(0.5, 0.5)
mat["t2",] <- c(0.5, 0.5)
mat
#> colnames
#> rownames A--TRUE A--FALSE
#> t1 1.0 0.0
#> t2 0.5 0.5
#> t3 0.0 1.0
#> t4 0.5 0.5
#> t5 1.0 0.0
#> t6 0.0 1.0
#> t7 0.5 0.5
models <- c('ER', 'ARD', 'SYM')
pis <- c('fitzjohn', 'equal', 'estimated')
plotList <- vector('list', length(models) * length(pis))
n <- 1
for (i in seq_along(models)) {
for (j in seq_along(pis)) {
fit <- fitMk(tree = randomTree, x = mat, model = models[i], pi = pis[j])
ace <- ancr(fit, tips = TRUE)
plotList[[n]] <- plotT(
randomTree, ace = ace, input_tips = c("t1", "t5", "t3", "t6"),
model = models[i], pi = pis[j]
)
n <- n + 1
}
}
#> Using pi estimated from the stationary distribution of Q assuming a flat prior.
#> pi =
#> A--TRUE A--FALSE
#> 0.5 0.5
#>
#> Using pi estimated from the stationary distribution of Q assuming a flat prior.
#> pi =
#> A--TRUE A--FALSE
#> 0.999934 0.000066
#>
#> Using pi estimated from the stationary distribution of Q assuming a flat prior.
#> pi =
#> A--TRUE A--FALSE
#> 0.5 0.5
mergedPlot <- ggarrange(plotlist = plotList, ncol = 3, nrow = 3)
mergedPlot
Session info
sessioninfo::session_info()
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#> setting value
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#> collate en_US.UTF-8
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#> date 2024-11-21
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