Claddis (version 0.7.0)

plot_rates_tree: Visualize a rate test time series

Description

Given the results from a rates test produces a time series visualization for a specific model.

Usage

plot_rates_tree(test_rates_output, model_type, model_number, ...)

Value

Nothing is returned, but a plot is produced.

Arguments

test_rates_output

Rate output from test_rates.

model_type

The type of model to plot. Must be one of "branch" or "clade".

model_number

The number of the model you wish to visualise from the rate output.

...

Other options to be passed to plot.

Author

Graeme T. Lloyd

Details

The raw output from test_rates can be difficult to interpret without visualization and this function provides a means for doing that when the desired output is a time series (other functions will be added for other types of rate test).

The function will only work for a single model, but in practice the user may wish to produce multiple plots in which case they simply need to rn the function multiple times or setup a multipanel window first with layout, or similar.

Plots use the geoscale package to add geologic time to the x-axis and interested users should consult the documentation tere for a full ist of options (passed via ...) in the function (see example below).

Calculated rates (changes per lineage million years) are plotted as filled circles and models are plotted as horizontal lines labelled by rate parameters (lambda_i).

Examples

# \donttest{
# Make time-scaled first MPT for Day 2016 data set:
time_tree <- ape::read.tree(text = paste0("(Biarmosuchus_tener:0.5,",
  "(((Hipposaurus_boonstrai:3.5,(Bullacephalus_jacksoni:0.75,",
  "Pachydectes_elsi:0.75):0.75):0.75,(Lemurosaurus_pricei:7.166666667,",
  "(Lobalopex_mordax:4.333333333,((Lophorhinus_willodenensis:3.666666667,",
  "(Proburnetia_viatkensis:0.8333333333,(Lende_chiweta:2,",
  "(Paraburnetia_sneeubergensis:1,Burnetia_mirabilis:2):1):1.833333333)",
  ":0.8333333333):0.8333333333,(BP_1_7098:2.25,Niuksenitia_sukhonensis:",
  "1.25):1.25):0.8333333333):0.8333333333):3.083333333):1.95,",
  "(Ictidorhinus_martinsi:15.9,(RC_20:11.6,(Herpetoskylax_hopsoni:11.3,",
  "Lycaenodon_longiceps:0.3):0.3):0.3):0.3):0.3);"))

# Add root age to tree:
time_tree$root.time <- 269.5

# Prune continuous block from day 2016:
cladistic_matrix <- prune_cladistic_matrix(
  cladistic_matrix = day_2016,
  blocks2prune = 1
)

# Generate nine two million year time bins:
time_bins <- matrix(data = c(seq(from = 270, to = 252, length.out = 10)[1:9],
  seq(from = 270, to = 252, length.out = 10)[2:10]), ncol = 2,
  dimnames = list(LETTERS[1:9], c("fad", "lad")))

# Set class as timeBins:
class(time_bins) <- "timeBins"

# Run test rates function for each clade partition:
test_rates_output <- test_rates(
  time_tree = time_tree,
  cladistic_matrix = cladistic_matrix,
  clade_partitions = as.list(x = seq(
    from = ape::Ntip(phy = time_tree) + 1,
    to = ape::Ntip(phy = time_tree) + ape::Nnode(time_tree), by = 1
  )),
  branch_partitions = lapply(X = as.list(x = seq(
    from = 1,
    to = length(x = time_tree$edge.length), by = 1
  )), as.list),
  time_bins = time_bins
)

# Plot ninth branch partition model (lowest AIC value):
plot_rates_tree(
  test_rates_output = test_rates_output,
  model_type = "branch", model_number = 9
)

# Plot third clade partition model (lowest AIC value):
plot_rates_tree(
  test_rates_output = test_rates_output,
  model_type = "clade", model_number = 3
)
# }