Plot survival along a record of temperature

Draws the survival predicted by predict_survival() or average_survival(): the probability of surviving to each time of the record, as the posterior median with its credible interval. The result is a ggplot object, to be refined by the user with further ggplot2 layers, scales, labels and themes.

Usage

plot_survival(
  x,
  temperature = FALSE,
  facet = TRUE,
  colour_by = NULL,
  interval = is.null(colour_by),
  time_unit = NULL
)

Arguments

x: A list, the output of predict_survival() or average_survival().
temperature: A single logical value, whether to draw the record of temperature, and of any co-stressor that changes along it, in panels above the survival curve. Defaults to FALSE.
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, with a colour per setting.
colour_by: An optional character string naming a numeric covariate of newdata, to colour the curves by.
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 setting of newdata has its own panel, following the same rule as in plot_tdt(). There are two ways to draw the settings together in one panel. With facet = FALSE each setting has its own colour, from teal to deep purple in the order of newdata, and an entry in the legend, which suits a few settings or a covariate that is not numeric; with more than eight settings the colours are too close to tell apart, and a message says to use panels or colour_by. With colour_by, the curves of every value of one numeric covariate are coloured on a continuous scale from teal for its lowest value to deep purple for its highest, and the intervals are left out by default, since they overlap.

With temperature = TRUE the record of temperature is drawn above the survival curve on the same time axis, so that a drop in survival can be read against the temperatures that caused it. A co-stressor the record follows through time is drawn in a panel of its own between the two, named by its column, when it changes along the record; one that stays constant is left out.

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))
# a two-day record read every hour, with a daily cycle
hours  <- 0:48
record <- data.frame(time = hours * 60, day = pmin(hours %/% 24, 1),
                     temp = 24 + 3 * sin(2 * pi * (hours - 9) / 24))

pred <- predict_survival(fit, record, newdata = data.frame(mass = c(8, 12)))
plot_survival(pred, time_unit = "min")
plot_survival(pred, temperature = TRUE, time_unit = "min")

# both masses in one panel, the record drawn once
plot_survival(pred, temperature = TRUE, facet = FALSE, time_unit = "min")
} # }