Fitted values for an Emax regression model — fitted.emaxlogistic • emaxnls

Returns the model predictions at the original data points. For emaxlogistic objects, the type argument controls whether fitted probabilities or the linear predictor on the logit scale are returned.

Usage

# S3 method for class 'emaxlogistic'
fitted(object, type = c("response", "link"), ...)

# S3 method for class 'emaxnls'
fitted(object, ...)

Arguments

object: An emaxnls or emaxlogistic object
type: For emaxlogistic objects: "response" (default) returns fitted probabilities; "link" returns the linear predictor on the logit scale. Ignored for emaxnls objects.
...: Ignored

Value

A numeric vector of fitted values, with length equal to the number of observations in the original data

Details

For emaxnls objects, these are the predicted values from the Emax curve evaluated at each observation's exposure and covariate values.

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)
)
fitted(mod_c)[1:20]
#>  [1] 14.500646 15.591349  5.647906 13.402470 13.561531 16.851552 17.150410
#>  [8] 14.795187  7.407757 12.975508 18.332700 15.783209  6.809797  6.352819
#> [15] 16.615000 16.302892  5.574984 16.150465 15.843635 14.834422

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)
)
fitted(mod_b)[1:20]
#>  [1] 0.70363536 0.90572875 0.01482219 0.39544147 0.53245238 0.98428451
#>  [7] 0.98792349 0.76673516 0.14039964 0.45187209 0.99777959 0.92563660
#> [13] 0.06771991 0.03763388 0.97539178 0.96761542 0.01344634 0.95359520
#> [19] 0.93105223 0.85936669
fitted(mod_b, type = "link")[1:20]
#>  [1]  0.8646697  2.2625636 -4.1966964 -0.4244957  0.1299923  4.1372680
#>  [7]  4.4043433  1.1899670 -1.8119747 -0.1931095  6.1078418  2.5215178
#> [13] -2.6222531 -3.2414902  3.6797588  3.3971523 -4.2955109  3.0228364
#> [19]  2.6029661  1.8100398