Plots a 2D simplex — plot

Plots a 2D simplex (a triangle with unit sides centered at the origin) onto which per-recurrence posterior probabilities of recrudescence, relapse, reinfection (or any other probability triplet summing to one) can be projected; see project2D() and Examples below.

Usage

plot_simplex(
  v.labels = c("Recrudescence", "Relapse", "Reinfection"),
  v.cutoff = 0.5,
  v.colours = c("yellow", "purple", "red"),
  plot.tri = TRUE,
  p.coords = NULL,
  p.labels = rownames(p.coords),
  p.labels.pos = 3,
  p.labels.cex = 1,
  ...
)

Arguments

v.labels: Vertex labels anticlockwise from top (default: "Recrudescence", "Relapse", "Reinfection"). If NULL, vertices are not labelled.
v.cutoff: Number between 0.5 and 1 that separates lower vs higher probability regions. Use with caution for recrudescence and reinfection classification; see Understand posterior probabilities.
v.colours: Vertex colours anticlockwise from top.
plot.tri: Logical; draws the triangular boundary if TRUE (default).
p.coords: Matrix of 3D simplex coordinates (e.g., per-recurrence probabilities of recrudescence, relapse and reinfection), one vector of 3D coordinates per row, each row is projected onto 2D coordinates using project2D() and plotted as a single simplex point using graphics::points(). If the user provides a vector encoding a probability triplet summing to one, it is converted to a matrix with one row.
p.labels: Labels of points in p.coords (default row names of p.coords) No labels if NA.
p.labels.pos: Position of p.labels: 1 = below, 2 = left, 3 = above (default) and 4 = right. Can be a single value or a vector.
p.labels.cex: Size expansion of p.labels passed to text.
...: Additional parameters passed to graphics::points().

Value

None

Examples

# Plot 2D simplex
plot_simplex(p.coords = diag(3),
             p.labels = c("(1,0,0)", "(0,1,0)", "(0,0,1)"),
             p.labels.pos = c(1,3,3))


# ==============================================================================
# Given data on an enrollment episode and a recurrence,
# compute the posterior probabilities of the 3Rs and plot the deviation of the
# posterior from the prior
# ==============================================================================

# Some data:
y <- list(list(m1 = c('a', 'b'), m2 = c('c', 'd')), # Enrollment episode
          list(m1 = c('a'), m2 = c('c'))) # Recurrent episode

# Some allele frequencies:
fs <- list(m1 = setNames(c(0.4, 0.6), c('a', 'b')),
           m2 = setNames(c(0.2, 0.8), c('c', 'd')))

# A vector of prior probabilities:
prior <- array(c(0.2, 0.3, 0.5), dim = c(1,3),
               dimnames = list(NULL, c("C", "L", "I")))

# Compute posterior probabilities
post <- compute_posterior(y, fs, prior, progress.bar = FALSE)
#> Number of valid relationship graphs (RGs) is 9
#> Computing log p(Y|RG) for 9 RGs
#> Finding log-likelihood of each vector of recurrence states
#> 

# Plot simplex with the prior and posterior
plot_simplex(p.coords = rbind(prior, post$marg),
             p.labels = c("Prior", "Posterior"),
             pch = 20)

# Add the deviation between the prior and posterior: requires obtaining 2D
# coordinates manually
xy_prior <- project2D(as.vector(prior))
xy_post <- project2D(as.vector(post$marg))
arrows(x0 = xy_prior["x"], x1 = xy_post["x"],
       y0 = xy_prior["y"], y1 = xy_post["y"], length = 0.1)