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Extract and plot EC50 residual diagnostics

Source: R/ec50-workflow.R
residual_data.Rd

`residual_data()` returns observed, fitted, and residual values from stored models. `plot_residuals()` returns a `ggplot2` diagnostic plot.

Usage

residual_data(x, models = "all")

plot_residuals(x, models = "all", type = c("fitted", "dose"))

Arguments

x

An object returned by [estimate_EC50()] or [ec50_multimodel()].

models

One of `"all"`, `"best"`, or a character vector of model names.

type

For `plot_residuals()`, plot residuals against fitted values or dose.

Value

`residual_data()` returns a data frame; `plot_residuals()` returns a `ggplot2` object.

Examples

data(multi_isolate)
fit <- estimate_EC50(
  growth ~ dose,
  data = subset(multi_isolate, isolate == 1 & fungicida == "Fungicide A"),
  isolate_col = "isolate",
  fct = drc::LL.3()
)
residual_data(fit)
#>    ID model  dose    observed     fitted     residual
#> 1   1  LL.3 0e+00 20.20823987 19.9257473  0.282492586
#> 2   1  LL.3 1e-05 20.11682791 19.8633829  0.253445024
#> 3   1  LL.3 1e-04 19.24796780 19.4416436 -0.193675790
#> 4   1  LL.3 1e-03 15.81234550 16.6397063 -0.827360808
#> 5   1  LL.3 1e-02  7.32067572  7.7646682 -0.443992436
#> 6   1  LL.3 1e-01  0.69852635  1.4846233 -0.786096958
#> 7   1  LL.3 1e+00  0.11811845  0.2002329 -0.082114404
#> 8   1  LL.3 0e+00 20.17927519 19.9257473  0.253527907
#> 9   1  LL.3 1e-05 19.74786350 19.8633829 -0.115519383
#> 10  1  LL.3 1e-04 19.50312642 19.4416436  0.061482830
#> 11  1  LL.3 1e-03 16.70972446 16.6397063  0.070018150
#> 12  1  LL.3 1e-02 10.68067905  7.7646682  2.916010892
#> 13  1  LL.3 1e-01  0.65167985  1.4846233 -0.832943465
#> 14  1  LL.3 1e+00  0.07631550  0.2002329 -0.123917353
#> 15  1  LL.3 0e+00 20.66666536 19.9257473  0.740918078
#> 16  1  LL.3 1e-05 19.32030445 19.8633829 -0.543078438
#> 17  1  LL.3 1e-04 20.23671428 19.4416436  0.795070696
#> 18  1  LL.3 1e-03 14.46034463 16.6397063 -2.179361683
#> 19  1  LL.3 1e-02  7.41380671  7.7646682 -0.350861442
#> 20  1  LL.3 1e-01  0.97172472  1.4846233 -0.512898592
#> 21  1  LL.3 1e+00  0.08066504  0.2002329 -0.119567808
#> 22  1  LL.3 0e+00 19.62249800 19.9257473 -0.303249285
#> 23  1  LL.3 1e-05 20.11220854 19.8633829  0.248825651
#> 24  1  LL.3 1e-04 18.60052509 19.4416436 -0.841118497
#> 25  1  LL.3 1e-03 16.84213793 16.6397063  0.202431618
#> 26  1  LL.3 1e-02  5.68736877  7.7646682 -2.077299378
#> 27  1  LL.3 1e-01  1.40551304  1.4846233 -0.079110268
#> 28  1  LL.3 1e+00  0.16302001  0.2002329 -0.037212840
#> 29  1  LL.3 0e+00 19.70929331 19.9257473 -0.216453980
#> 30  1  LL.3 1e-05 19.85658256 19.8633829 -0.006800328
#> 31  1  LL.3 1e-04 19.67191918 19.4416436  0.230275589
#> 32  1  LL.3 1e-03 18.17009348 16.6397063  1.530387172
#> 33  1  LL.3 1e-02  9.08760034  7.7646682  1.322932191
#> 34  1  LL.3 1e-01  1.44357318  1.4846233 -0.041050130
#> 35  1  LL.3 1e+00  0.08274680  0.2002329 -0.117486046
plot_residuals(fit)