Plot each period's share of the risk along a record

Draws the risk table of predict_survival() or average_survival(): the share of the hazard accrued over the record that falls in each period, as a bar at the posterior median with its credible interval. It shows where along the record the risk is concentrated. The result is a ggplot object, which can be refined by the user with further ggplot2 layers, scales, labels and themes.

Usage

plot_risk(x, facet = TRUE, interval = TRUE)

Arguments

x

A list, the output of predict_survival() or average_survival() called with by.

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 the settings side by side in each period.

interval

A single logical value, whether to draw the credible interval. Defaults to TRUE.

Value

A ggplot object.

Details

The shares are of whatever risk there is, so they say where it falls and not how large it is; read them with the mortality table. A period has no bar where no posterior draw has any hazard along the record.

By default each setting of newdata has its own panel, following the same rule as in plot_tdt(). With facet = FALSE the settings are drawn side by side within each period, each in its own colour, from teal to deep purple in the order of newdata; this needs eight settings or fewer.

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)),
                         by = "day")
plot_risk(pred)
plot_risk(pred, facet = FALSE)
} # }