Dynamic critical thermal maximum averaged over several fitted models

Derives the dynamic critical thermal maximum dCTmax under a ramp as derive_dctmax() does, averaged over several models fitted to the same data. Each model's posterior draws of dCTmax 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 predicts best.

Usage

average_dctmax(
  fits,
  weights,
  rate,
  T_start,
  newdata = NULL,
  p = 0.5,
  levels = NULL,
  prob = 0.95,
  n_draws = 1000,
  T_max = NULL
)

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.

rate

A numeric vector of ramp rates, in degrees per unit of time of the data the model was fitted to (e.g. degrees C per minute), all positive.

T_start

A numeric vector of starting temperatures, one per rate or a single one for all.

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.

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.

T_max

A single number, the temperature up to which the ramp is followed. NULL (the default) takes 15 degrees above the warmest temperature the models were fitted to.

Value

A list of two tibbles: dctmax, in the layout of derive_dctmax()'s result, and weights, the table of weights with the number of draws each model contributed.

Details

The models usually agree closely on dCTmax under a ramp like those they were fitted to, and less under ramps much slower or faster, where the failure-rate and baseline functions are extrapolated.

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))
average_dctmax(fits, w, rate = c(0.1, 0.3), T_start = 14,
               newdata = data.frame(mass = 10))$dctmax
} # }