Predicting from Emax regression models — predict.emaxlogistic • emaxnls

Generates predictions from a fitted Emax model, either at the original data points or at new covariate and exposure values supplied via newdata. Standard errors and confidence or prediction intervals can be requested.

Usage

# S3 method for class 'emaxlogistic'
predict(
  object,
  newdata = NULL,
  type = c("response", "link"),
  se.fit = FALSE,
  interval = "none",
  level = 0.95,
  ...
)

# S3 method for class 'emaxnls'
predict(
  object,
  newdata = NULL,
  se.fit = FALSE,
  interval = "none",
  level = 0.95,
  ...
)

Arguments

object: An emaxnls or emaxlogistic object
newdata: A named list or data frame in which to look for variables with which to predict. If newdata is missing the fitted values at the original data points are returned.
type: For emaxlogistic objects: "response" (default) returns predicted probabilities; "link" returns the linear predictor on the logit scale. Ignored for emaxnls objects.
se.fit: A switch indicating if standard errors are required.
interval: A character string indicating if prediction intervals or a confidence interval on the mean responses are to be calculated. Can be "none", "confidence", or "prediction".
level: A numeric scalar between 0 and 1 giving the confidence level for the intervals (if any) to be calculated.
...: Ignored

Value

The return value differs slightly depending on inputs. When se.fit = FALSE, it produces a vector or matrix of predictions with column names fit, lwr and upr if the interval argument is set. When se.fit = TRUE, it returns a list with the following components:

fit: vector or matrix as above
se.fit: standard error of the predicted means
residual.scale: residual standard deviation
df: residual degrees of freedom

Details

For emaxlogistic objects, when interval is set, the bounds are first computed on the link scale and then passed through the inverse logit transformation, ensuring they remain in the unit interval on the probability scale.

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)
)

# return a vector of predictions
predict(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

# return a matrix with confidence intervals
predict(mod_c, interval = "confidence", se.fit = FALSE)
#> # A tibble: 400 × 3
#>      fit   lwr   upr
#>    <dbl> <dbl> <dbl>
#>  1 14.5  14.4  14.6 
#>  2 15.6  15.5  15.7 
#>  3  5.65  5.52  5.78
#>  4 13.4  13.3  13.5 
#>  5 13.6  13.5  13.6 
#>  6 16.9  16.7  17.0 
#>  7 17.2  17.0  17.3 
#>  8 14.8  14.7  14.9 
#>  9  7.41  7.31  7.51
#> 10 13.0  12.9  13.1 
#> # ℹ 390 more rows

# emaxlogistic predicted 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)
)
predict(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
predict(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