Residuals for an Emax regression model — residuals.emaxlogistic • emaxnls

For emaxnls objects, returns raw residuals on the response scale. For emaxlogistic objects, Pearson or deviance residuals are available via the type argument.

Usage

# S3 method for class 'emaxlogistic'
residuals(object, type = c("pearson", "deviance"), ...)

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

Arguments

object: An emaxnls or emaxlogistic object
type: For emaxlogistic objects: the type of residuals to return. "pearson" (default) returns Pearson residuals; "deviance" returns deviance residuals. Ignored for emaxnls objects.
...: Ignored

Value

A numeric vector of residuals

Details

Pearson residuals are the raw residuals divided by sqrt(mu * (1 - mu)), the estimated standard deviation of a Bernoulli observation, giving a standardized measure of discrepancy. Deviance residuals are the signed square root of each observation's contribution to the total binomial deviance; their sum of squares equals the model deviance returned by deviance().

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)
)
residuals(mod_c)[1:20]
#>  [1]  1.16935390 -0.33134886  0.00209353 -0.94247006 -0.31153056 -0.05155200
#>  [7]  0.24959037  1.07481254 -0.10775738 -0.15550832 -0.14269996  0.56679081
#> [13] -0.91979699 -0.56281913  0.64500044  0.56710765 -0.44498447  0.47953472
#> [19]  0.07636540 -0.19442164

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)
)
residuals(mod_b)[1:20]
#>  [1]  0.6489920  0.3226195 -0.1226589 -0.8087642  0.9370711  0.1263583
#>  [7]  0.1105628 -1.8130010 -0.4041427  1.1013699  0.0471736  0.2834388
#> [13] -0.2695163 -0.1977513  0.1588366  0.1829438 -0.1167459  0.2205969
#> [19]  0.2721279  0.4045338
residuals(mod_b, type = "deviance")[1:20]
#>  [1]  0.83844501  0.44500653 -0.17281864 -1.00325150  1.12273043  0.17799039
#>  [7]  0.15588470 -1.70621267 -0.55006853  1.26044129  0.06667647  0.39312482
#> [13] -0.37449161 -0.27698491  0.22323109  0.25659526 -0.16454519  0.30827265
#> [19]  0.37799446  0.55056257