The fitted failure rate over temperature, for each posterior draw

Evaluates the failure rate M(T) of a model fitted with fit_hazard() at each temperature in Ta, for every posterior draw, at the covariate values in each row of newdata. The rate is computed from the same expression the model was fitted with, and the failure-rate function, reference temperature, softening steepness and co-stressors are read from the fit, so none of them is entered by hand. Random effects are set to zero, so the rate is that of a typical group.

Usage

M_draws(fit, Ta, newdata = NULL, levels = NULL, n_draws = NULL)

Arguments

fit

A brmsfit, a model fitted with fit_hazard().

Ta

A numeric vector of temperatures at which to evaluate the rate.

newdata

A data frame of covariate values, one row per setting at which to evaluate the rate. It must hold every covariate in the model's parameter formulas; other columns the model needs are filled in. May be NULL for a model without covariates.

levels

A named numeric vector giving the level of each co-stressor, on its measurement scale, e.g. c(DO = 6) for 6 mg/L of dissolved oxygen. Required when the model has co-stressors.

n_draws

An optional single number, the number of posterior draws to keep, evenly spaced through the posterior. NULL (the default) keeps them all.

Value

A tibble with one row per draw, temperature and row of newdata: the columns of newdata, .draw (the draw's number in the fit, so draws from different calls can be matched), Ta and the failure rate M.

Details

The draws are returned as they are, not summarised, for plotting the rate with its uncertainty or for further calculation.

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))

rate <- M_draws(fit, Ta = seq(25, 40, by = 0.5),
                newdata = data.frame(mass = c(8, 12)))
} # }