Regresses the valid Stage-1 log10(LT50) estimates on assay temperature by
ordinary least squares and derives the classical quantities. z = -1/slope;
CTmax(t_ref) = (log10(t_ref) - intercept) / slope; T_crit follows the
rate-multiplier definition, CTmax + z * mean(log10(TC_rate_range/100)).
t_ref is expressed in minutes and is recorded in the returned $settings.
Thus summary$CTmax is always CTmax at the explicitly recorded reference
duration, not necessarily CTmax at one hour. Stage 2 retains only rows chosen
by rows; if fewer than three valid Stage-1 temperatures remain, it returns
a NULL fit and an NA summary rather than extrapolating a TDT line.
Arguments
- stage1
Output of
ts_stage1().- t_ref
Reference exposure duration for CTmax (minutes). Default 60.
- time_multiplier
Multiplier from the Stage-1 duration unit to minutes (e.g. 60 if durations are in hours). Default 1.
- TC_rate_range
Length-2 HI-rate range (% per hour) for T_crit.
- rows
Which Stage-1 rows to keep:
"stage1_ok"(bracketing validation, the case-study default) or"finite_ok"(finite/negative slope only, the simulation's looser rule).
Value
list(fit, summary, settings); fit is NULL if fewer than 3 valid
Stage-1 estimates remain. summary has intercept, slope_T, z,
CTmax, T_crit, r_squared, n_stage1, n_excluded. $settings
records t_ref, time_multiplier, and TC_rate_range; downstream
ts_ci() and ts_curve() inherit these settings by default.
For compatibility, when t_ref = 60 exactly, summary also contains a
truthful CTmax_1hr alias for CTmax; it is absent at every other
reference duration.
Examples
d <- simulate_tls(
family = "binomial", temps = seq(30, 42, by = 2),
times = c(0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30, 100),
reps = 4, n = 50, CTmax = 36, z = 4, seed = 42
)
s1 <- ts_stage1(d, "temp", "duration", "survived", "total")
ts_stage2(s1, t_ref = 60, time_multiplier = 60)$summary
#> # A tibble: 1 × 9
#> intercept slope_T z CTmax T_crit r_squared n_stage1 n_excluded CTmax_1hr
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int> <dbl>
#> 1 10.6 -0.245 4.08 36.0 25.8 0.999 7 0 36.0