Thermal tolerance averaged over several fitted models

Derives the static critical thermal maximum sCTmax, the thermal death time curves and the thermal tolerance landscape as derive_tdt_ttl() does, averaged over several models fitted to the same data. Each model's posterior draws of a quantity are pooled in proportion to its weight, and the pooled draws are summarised, so the median and credible interval reflect both the uncertainty within each model and the uncertainty about which model holds.

Usage

average_tdt_ttl(
  fits,
  weights,
  newdata = NULL,
  p = c(0.1, 0.5, 0.9),
  t_ref,
  Ta = NULL,
  tau = NULL,
  levels = NULL,
  prob = 0.95,
  n_draws = 1000
)

Arguments

fits: A named list of brmsfits, models fitted with fit_hazard() to the same data.
weights: The weights from hazard_weights(), whose model names are names of fits. Only the models it kept are averaged.
newdata: A data frame of covariate values, one row per setting at which to derive. It must hold every covariate in the model's parameter formulas. May be NULL for a model without covariates.
p: A numeric vector of the fractions of individuals failing at which sCTmax and the thermal death time curves are derived, each strictly between 0 and 1.
t_ref: A single positive number, the reference exposure time for sCTmax, in the time unit of the data the model was fitted to (for example 60 for one hour when times are in minutes). It has no default, because the fit does not record that unit.
Ta: An optional numeric vector of the constant temperatures for the thermal death time curves and the tolerance landscape. NULL (the default) takes 50 evenly spaced across the temperatures the data cover.
tau: An optional numeric vector of the elapsed exposure times for the tolerance landscape, in the time unit of the data, none negative. NULL (the default) takes 50 spaced evenly on a log scale across the times the data cover. A time of zero is allowed, and gives a survival of 1, so that a survival curve can start there.
levels: A named numeric vector giving the level of each co-stressor, on its measurement scale, e.g. c(DO = 6). Required when the model has co-stressors.
prob: A single number strictly between 0 and 1, the coverage of the credible intervals.
n_draws: A single number, the number of posterior draws in the pool. Each model contributes its share of them, evenly spaced through its posterior. Where a model holds fewer draws than its share, the pool is made smaller so that the shares stay in proportion to the weights, and a message says so.

Value

A list of four tibbles: sctmax, tdt_curves and ttl_surface as from derive_tdt_ttl(), and weights, the table of weights with the number of draws each model contributed.

Details

The parameters are not averaged. The parameters of different failure-rate functions are different quantities under the same names, so a pooled a or b would mean nothing; they are reported for each model by derive_tdt_ttl().

The result has the layout of derive_tdt_ttl()'s, without its params table, so plot_tdt(), plot_ttl() and plot_ttl_3d() draw it as they draw a single model's.

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_mem     <- fit_hazard(long, "exp", dpar_formulas = list(loga = ~ 1 + mass))
fit_weibull <- fit_hazard(long, "exp", baseline = "weibull",
                          dpar_formulas = list(loga = ~ 1 + mass))
fits        <- list(memoryless = fit_mem, weibull = fit_weibull)

w    <- hazard_weights(lapply(fits, loo_hazard))
avg  <- average_tdt_ttl(fits, w, newdata = data.frame(mass = 10), t_ref = 60)
plot_tdt(avg)
} # }