Skip to contents

Build a freqTLS 4PL formula from the direct CTmax/z interface

Source: R/fit_4pl.R
make_4pl_formula.Rd

Translates the bayesTLS-style direct-mode arguments (ctmax, z, up, low, k, by) into the engine's tls_bf() tls_formula object. Supplying a ctmax and/or z formula is the direct parameterisation; by is shorthand for fixed cell means of CTmax and z by one moderator (~ 0 + by); it does not add random effects or modify the shape formulas.

Usage

make_4pl_formula(
  ctmax = NULL,
  z = NULL,
  up = NULL,
  low = NULL,
  k = NULL,
  by = NULL,
  family = "beta_binomial"
)

Arguments

ctmax, z, up, low, k

One-sided formulas (or NULL). ctmax/z set the CTmax / thermal-sensitivity structure and must have the same fixed-effect model-matrix columns; up/low/k set the 4PL shape. Random intercepts are supported on ctmax, z, low, and k, but not up.

by

Optional single, non-missing moderator column name. It is shorthand for ctmax = z = ~ 0 + by when those are not given explicitly; it does not add random effects or modify low, up, or k.

family

"beta_binomial", "binomial", or "beta" (selects the response idiom: n_surv | trials(n_total) for counts, bare survival for the continuous-proportion beta family).

Value

A tls_formula object (as built by tls_bf()).

Details

Following the freqTLS constant-shape invariant, the asymptotes and steepness (up, low, k) default to shared (~ 1) so the temperature effect runs through the midpoint (CTmax / z) only; pass an explicit formula to let a shape vary. ctmax and z must produce the same fixed-effect model-matrix columns. Supported random intercepts go inside the ctmax/z/low/k formulas; up random effects are not supported. For example, ctmax = ~ 1 + (1 | batch) keeps the same intercept-only fixed design as the default z = ~ 1 while adding a CTmax random intercept.

See also

fit_4pl(), tls_bf(), standardize_data()

Examples

make_4pl_formula()
#> <tls_formula>
#> n_surv | trials(n_total) ~ time(duration) + temp(temp)
#> low ~ 1
#> up ~ 1
#> log_k ~ 1
#> CTmax ~ 1
#> log_z ~ 1
make_4pl_formula(by = "population", family = "binomial")
#> <tls_formula>
#> n_surv | trials(n_total) ~ time(duration) + temp(temp)
#> low ~ 1
#> up ~ 1
#> log_k ~ 1
#> CTmax ~ 0 + population
#> log_z ~ 0 + population