Construct Emax prediction function from model object

Extracts a customizable prediction function from a fitted Emax model, allowing predictions to be evaluated at arbitrary data and parameter values.

Usage

emax_fun(mod, ...)

# S3 method for class 'emaxlogistic'
emax_fun(mod, ...)

# S3 method for class 'emaxnls'
emax_fun(mod, ...)

Arguments

mod

An emaxnls or emaxlogistic object

...

Ignored

Value

A function with arguments param and data that evaluates the Emax model at the supplied (or default) parameter values and data. For emaxnls objects the return values are on the response scale; for emaxlogistic objects they are predicted probabilities in \((0, 1)\).

Details

The extracted function accepts data and param arguments. Both default to the values used when fitting the model. When supplying custom values, data must contain all variables used by the model, and param must be a named numeric vector whose names exactly match those returned by coef(mod).

Scale of predictions. For emaxnls objects the returned function produces predictions on the response scale (the same scale as the outcome variable). For emaxlogistic objects the structural Emax model is parameterized on the logit scale — logit(p) = E0 + Emax * x / (x + EC50) — but emax_fun() applies the inverse-logit transformation before returning, so predictions are on the probability scale. This is consistent with the default behavior of fitted() and predict() for emaxlogistic objects. If you need the linear predictor (logit scale) directly, use fitted(object, type = "link") or predict(object, type = "link").

See also

emax_nls(), emax_logistic()

Examples

mod_c <- emax_nls(
  structural_model = rsp_1 ~ exp_1, 
  covariate_model = list(E0 ~ cnt_a, Emax ~ 1, logEC50 ~ 1), 
  data = emax_df,
  opts = emax_nls_options(max_time = 10)
)

if (emax_converged(mod_c)) {

  par <- coef(mod_c)
  mod_fn <- emax_fun(mod_c)
  
  # apply the function to a few rows of the original data
  mod_fn(data = emax_df[120:125, ], param = par)
  
  # adjust the parameters and re-evaluate
  new_par <- par
  new_par["E0_Intercept"] <- 0
  mod_fn(data = emax_df[120:125, ], param = new_par)

}
#> [1]  9.595929  3.412750  8.208382 10.674149  1.200782  8.567196

# for emaxlogistic, the returned function gives probabilities
mod_b <- emax_logistic(
  structural_model = rsp_2 ~ exp_1,
  covariate_model = list(E0 ~ cnt_a, Emax ~ 1, logEC50 ~ 1),
  data = emax_df,
  opts = emax_logistic_options(max_time = 10)
)

if (emax_converged(mod_b)) {
  mod_fn_b <- emax_fun(mod_b)
  mod_fn_b(data = emax_df[120:125, ])
}
#> [1] 0.73738728 0.40712302 0.38375750 0.92637415 0.03314224 0.39944030