library(Rphylopars)
We start this tutorial by simulating 2 traits by Brownian motion (BM) model without missing values. We did not simulate missing values because Rphylopars::simtraits deletes values from all traits. There is not a constraint to delete values from one trait. Then, we assigned NA values randomly to 10 values of the first simulated trait.
set.seed(2345)
sim <- Rphylopars::simtraits(ntaxa = 100,
ntraits = 2,
nreps = 1,
nmissing = 0,,
#v = matrix(c(1, 0.6, 0.6, 1), nrow = 2),
model = "BM",
nsim = 1
)
trait <- sim$trait_data
trait[sample(1:100, 10), 2] <- NAHere, missing values were imputed by a Brownian motion model.
phylopars_res <- Rphylopars::phylopars(trait_data = trait,
tree = sim$tree,
model = "BM",
phylo_correlated = TRUE,
pheno_error = FALSE,
pheno_correlated = FALSE
)Imputed values are found in the anc_recon element from the phylopars object. phylopars estimate values to all tree nodes. Below, there were filtered the imputed missing values.
anc <- phylopars_res$anc_recon[1:100, ]
obs_imput <- data.frame(imputed = anc[, 1],
original = sim$trait_data[, 2],
trait2 = sim$trait_data[, 3])
obs_imput <- obs_imput[is.na(trait[, 2]), ]This plot shows original (black points) and imputed (red circles) values. The red lines show the distance between the original and imputed values.
plot(x = sim$trait_data[, 3],
y = sim$trait_data[, 2],
pch = 19,
ylim = c(min(sim$trait_data[, 2]), max(sim$trait_data[, 2])),
xlab = "trait2",
ylab = "missing value trait"
)
points(x = trait[is.na(trait[, 2]), 3],
y = anc[is.na(trait[, 2]), 1],
col = "red")
lapply(1:nrow(obs_imput),
FUN = function(i) segments(x0 = obs_imput[i, 3],
y0 = obs_imput[i, 2],
x1 = obs_imput[i, 3],
y1 = obs_imput[i, 1], col = "red"
)
)
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