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Predict the fitted survival surface with bootstrap confidence bands

Source: R/predict_survival_curves.R
predict_survival_curves.Rd

The frequentist analogue of bayesTLS::predict_survival_curves(). Evaluates the fitted 4PL survival probability over a temperature-by-duration grid and adds parametric-bootstrap confidence bands. For random-effects fits the curves are population-level: random intercepts are integrated during bootstrap refits, but no fitted group BLUP is added to the reported curve.

Usage

predict_survival_curves(
  object,
  temps = NULL,
  durations = NULL,
  nboot = 500L,
  level = 0.95,
  seed = NULL,
  by = NULL
)

Arguments

object

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

temps

Temperatures to predict at (default: the observed assay temps).

durations

Exposure durations (default: 100 points log-spaced over the observed range, in the data's duration unit).

nboot

Number of bootstrap replicates for the bands (default 500). Bootstrap bands currently require shared fixed-effect shape formulas (low = up = log_k = ~ 1). For varying shapes, use predict() for the fitted surface; a design-aware bootstrap surface is not yet available.

level

Confidence level (default 0.95).

seed

Optional RNG seed.

by

Optional name for the grouping column.

Value

A freq_surv_curves object: $summary (a tibble of [<group>,] temp, duration, survival_lower, survival_median, survival_upper) and $meta.

See also

fit_4pl(), predict_survival_surface(), tls()

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)
curves <- predict_survival_curves(
  fit, temps = c(34, 36), durations = c(1, 4), nboot = 10, seed = 1
)
curves$summary
#> # A tibble: 4 × 5
#>    temp duration survival_lower survival_median survival_upper
#>   <dbl>    <dbl>          <dbl>           <dbl>          <dbl>
#> 1    34        1         0.868           0.888          0.919 
#> 2    36        1         0.433           0.470          0.518 
#> 3    34        4         0.331           0.363          0.399 
#> 4    36        4         0.0452          0.0641         0.0773
# }