Plot the thermal death time curves

Draws the thermal death time curves from derive_tdt_ttl(): the time until a fraction p of individuals has failed, against constant temperature, as the posterior median with its credible interval. The result is a ggplot object, which can be refined by the user with further ggplot2 layers, scales, labels and themes.

Usage

plot_tdt(
  x,
  facet = TRUE,
  colour_by = NULL,
  interval = is.null(colour_by),
  time_unit = NULL
)

Arguments

x

A list, the output of derive_tdt_ttl().

facet

A single logical value or the character string "all". TRUE (the default) gives a panel per setting of newdata, cut down to quartile values when there are many; "all" a panel for every setting; FALSE no panels.

colour_by

An optional character string naming a numeric covariate of newdata, to colour the curves by instead of by p.

interval

A single logical value, whether to draw the credible interval. Defaults to TRUE, and to FALSE with colour_by.

time_unit

An optional character string giving the time unit for the axis label, e.g. "min". The fit does not record the unit of its times, so none is shown unless given.

Value

A ggplot object.

Details

By default each fraction p has its own colour, from blue for the fewest failed to red for the most, and each setting of newdata its own panel. Each setting has its own curve, so settings are shown or left out, never averaged: with more than six, a numeric covariate with more than five values is cut down to the values nearest its minimum, quartiles and maximum, and a message says which were kept.

With colour_by, the curves of every value of one numeric covariate are drawn together, coloured on a continuous scale from teal for its lowest value to deep purple for its highest, with a panel per fraction p. This shows how the curve shifts with the covariate. The intervals are then left out by default, since they overlap. Where the covariate is itself a temperature, such as an acclimation temperature, a cool-to-warm scale may read better, and can be added to the result: + ggplot2::scale_colour_gradient(low = "#2C7BB6", high = "#D7191C").

Time is on a log scale, on which an exponential failure rate gives straight lines. Where a share of the posterior never reaches the fraction, the interval runs to the top of the panel, and where the median itself is never reached the line stops.

Examples

if (FALSE) { # \dontrun{
# a model fitted to a small ramping trial: 20 individuals warmed from 20 to 35
# degrees over 60 minutes and checked every 10, in two trials (see fit_hazard())
path   <- data.frame(path_id = "A", time = c(0, 60), temp = c(20, 35))
checks <- data.frame(path_id = "A", time = seq(0, 60, by = 10))
ind    <- data.frame(id = 1:20, path_id = "A", event = 1, mass = runif(20, 8, 12),
                     trial = rep(c("a", "b"), each = 10),
                     t_upper = sample(seq(10, 60, 10), 20, replace = TRUE))
long   <- build_hazard_data(ind, path, "interval", covariates = c("mass", "trial"),
                            checks = checks)
fit    <- fit_hazard(long, "exp", dpar_formulas = list(loga = ~ 1 + mass))

tol <- derive_tdt_ttl(fit, newdata = data.frame(mass = c(8, 12)), t_ref = 60)
plot_tdt(tol, time_unit = "min")

# how the curves shift with body mass
tol <- derive_tdt_ttl(fit, newdata = data.frame(mass = seq(5, 15, by = 0.5)),
                      t_ref = 60)
plot_tdt(tol, colour_by = "mass", time_unit = "min")
} # }