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Extract z, CTmax and (optionally) T_crit with bootstrap confidence intervals

Source: R/extract_tdt.R
extract_tdt.Rd

The frequentist analogue of bayesTLS::extract_tdt(). Runs a parametric bootstrap (via the freqTLS engine), derives the thermal-death-time quantities on each replicate, and returns the same nested $z / $CTmax / $T_crit structure (each a list of draws + summary). Each row of a per-replicate table is a derived estimate from one parametric-bootstrap refit: it is a resampled frequentist estimate, not a posterior draw. *_median is the maximum-likelihood point estimate and *_lower / *_upper are bootstrap percentiles.

Usage

extract_tdt(
  object,
  target_surv = "relative",
  lethal = FALSE,
  TC_rate_range = c(0.1, 1),
  nboot = 1000L,
  level = 0.95,
  seed = NULL,
  by = NULL
)

Arguments

object

A freq_tls fit from fit_4pl() (or a profile_tls fit).

target_surv

"relative" (curve midpoint, default), "absolute" (50% survival), or a numeric survival level in (0, 1) for an LTx CTmax. An absolute target must lie strictly between the fitted asymptotes for every reported group; otherwise no finite crossing exists.

lethal

If TRUE, also derive T_crit (the damage-rate-floor critical temperature, CTmax + z * log10(rate / 100), rate log-uniform over TC_rate_range), anchored at the fit's reference time.

TC_rate_range

Damage-rate floor range (percent of lethal dose per hour) for T_crit. Default c(0.1, 1).

nboot

Number of bootstrap replicates (default 1000; smaller is faster).

level

Confidence level (default 0.95).

seed

Optional RNG seed for reproducible replicates / rate draws.

by

Optional name for the grouping column; defaults to the fit moderator.

Value

A list with $z, $CTmax, ($T_crit when lethal), and $meta. Each quantity is list(draws = <tibble>, summary = <tibble>). Column names follow bayesTLS: z_median/z_lower/z_upper for z; temp_median/temp_lower/ temp_upper for CTmax and T_crit; per-draw value columns are z / temp. $meta$tref records the reference time; $meta$duration_unit is retained when the input is a fit_4pl() workflow.

See also

fit_4pl(), tls(), get_z_summary(), get_ctmax_summary()

Examples

# \donttest{
raw <- simulate_tls(family = "binomial", CTmax = 36, z = 4, seed = 1)
dat <- standardize_data(
  raw, temp = "temp", duration = "duration",
  n_total = "total", n_surv = "survived"
)
fit <- fit_4pl(dat, family = "binomial", t_ref = 1, quiet = TRUE)
tdt <- extract_tdt(fit, nboot = 10, seed = 1)
tdt$CTmax$summary
#> # A tibble: 1 × 3
#>   temp_median temp_lower temp_upper
#>         <dbl>      <dbl>      <dbl>
#> 1        35.9       35.8       36.1
# }