Predict a survival surface over a temperature-by-duration grid

predict_survival_surface() evaluates the fitted survival probability on a factorial grid of temperatures by durations, returning a long data frame suitable for a heatmap or contour plot (see plot_survival_surface()). For random-effects fits this helper returns population-level predictions (random intercepts set to zero); use predict(..., re.form = "conditional") for known-group conditional predictions. General continuous fixed designs require predict() with their covariate columns supplied in newdata.

Usage

predict_survival_surface(object, temps = NULL, times = NULL, group = NULL)

Arguments

object: A profile_tls fit from fit_tls().
temps: Numeric vector of temperatures. Defaults to a length-60 sequence spanning the fit's observed temperature range.
times: Numeric vector of durations (strictly positive). Defaults to a length-60 log-spaced sequence spanning the fit's observed duration range.
group: Optional single group level (grouped fits only). When NULL (default) the surface is built for every group level and the result carries a group column.

Value

A long data.frame with columns temp, duration, survival (and group when the fit is grouped).

Examples

d <- simulate_tls(family = "binomial", CTmax = 36, z = 4, seed = 1)
fit <- fit_tls(d, y = survived, n = total, time = duration, temp = temp,
               family = "binomial", tref = 1)
head(predict_survival_surface(fit, temps = c(34, 36, 38), times = c(1, 2, 4)))
#>   temp duration   survival
#> 1   34        1 0.89445180
#> 2   36        1 0.47691987
#> 3   38        1 0.09003950
#> 4   34        2 0.69738630
#> 5   36        2 0.18571146
#> 6   38        2 0.03695279