ROCCurveAtomic: Atomic ROC curve
In plotthis: High-Level Plotting Built Upon 'ggplot2' and Other Plotting Packages

ROCCurveAtomic

R Documentation

Atomic ROC curve

Description

Atomic ROC curve

Usage

ROCCurveAtomic(
  data,
  truth_by,
  score_by,
  pos_label = NULL,
  group_by = NULL,
  group_by_sep = "_",
  group_name = NULL,
  x_axis_reverse = FALSE,
  percent = FALSE,
  ci = NULL,
  n_cuts = 0,
  cutoffs_at = NULL,
  cutoffs_labels = NULL,
  cutoffs_accuracy = 0.01,
  cutoffs_pt_size = 5,
  cutoffs_pt_shape = 4,
  cutoffs_pt_stroke = 1,
  cutoffs_labal_fg = "black",
  cutoffs_label_size = 4,
  cutoffs_label_bg = "white",
  cutoffs_label_bg_r = 0.1,
  show_auc = c("auto", "none", "legend", "plot"),
  auc_accuracy = 0.01,
  auc_size = 4,
  increasing = TRUE,
  theme = "theme_this",
  theme_args = list(),
  palette = "Spectral",
  palcolor = NULL,
  alpha = 1,
  facet_by = NULL,
  facet_scales = "fixed",
  facet_ncol = NULL,
  facet_nrow = NULL,
  facet_byrow = TRUE,
  aspect.ratio = 1,
  legend.position = waiver(),
  legend.direction = "vertical",
  title = NULL,
  subtitle = NULL,
  xlab = ifelse(x_axis_reverse, "Specificity", "1 - Specificity"),
  ylab = "Sensitivity",
  ...
)

Arguments

`data`	A data frame with the truth and score columns. See also https://cran.r-project.org/web/packages/plotROC/vignettes/examples.html.
`truth_by`	A character string of the column name that contains the true class labels. (a.k.a. the binary outcome, 1/0 or TRUE/FALSE.)
`score_by`	character strings of the column names that contains the predicted scores. When multiple columns are provided, the ROC curve is plotted for each column.
`pos_label`	A character string of the positive class label. When NULL, the labels will be handled by the `plotROC` package.
`group_by`	A character vector of column names to group the ROC curve by. When `score_by` contains multiple columns, `group_by` should be NULL.
`group_by_sep`	A character string to separate the columns in `group_by`.
`group_name`	A character string to name the legend of the ROC curve groups.
`x_axis_reverse`	A logical to reverse the x-axis, that is from 1 to 0.
`percent`	A logical to display the x and y axis as percentages.
`ci`	A list of arguments to pass to `plotROC::geom_rocci()` to add confidence intervals. When NULL, no confidence intervals are added.
`n_cuts`	An integer to specify the number of cutpoints on the ROC curve. It will be the quantiles of the predicted scores.
`cutoffs_at`	Vector of user supplied cutoffs to plot as points. If non-NULL, it will override the values of n_cuts and plot the observed cutoffs closest to the user-supplied ones. Both `cutoffs_at` and `cutoffs.labels` will be passed to `plotROC::geom_roc()`. Other than numeric values, the following special values are allowed. These values are the methods of `OptimalCutpoints::optimal.cutpoints()`, they are literally: "CB" (cost-benefit method); "MCT" (minimizes Misclassification Cost Term); "MinValueSp" (a minimum value set for Specificity); "MinValueSe" (a minimum value set for Sensitivity); "ValueSe" (a value set for Sensitivity); "MinValueSpSe" (a minimum value set for Specificity and Sensitivity); "MaxSp" (maximizes Specificity); "MaxSe" (maximizes Sensitivity); "MaxSpSe" (maximizes Sensitivity and Specificity simultaneously); "MaxProdSpSe" (maximizes the product of Sensitivity and Specificity or Accuracy Area); "ROC01" (minimizes distance between ROC plot and point (0,1)); "SpEqualSe" (Sensitivity = Specificity); "Youden" (Youden Index); "MaxEfficiency" (maximizes Efficiency or Accuracy, similar to minimize Error Rate); "Minimax" (minimizes the most frequent error); "MaxDOR" (maximizes Diagnostic Odds Ratio); "MaxKappa" (maximizes Kappa Index); "MinValueNPV" (a minimum value set for Negative Predictive Value); "MinValuePPV" (a minimum value set for Positive Predictive Value); "ValueNPV" (a value set for Negative Predictive Value); "ValuePPV" (a value set for Positive Predictive Value); "MinValueNPVPPV" (a minimum value set for Predictive Values); "PROC01" (minimizes distance between PROC plot and point (0,1)); "NPVEqualPPV" (Negative Predictive Value = Positive Predictive Value); "MaxNPVPPV" (maximizes Positive Predictive Value and Negative Predictive Value simultaneously); "MaxSumNPVPPV" (maximizes the sum of the Predictive Values); "MaxProdNPVPPV" (maximizes the product of Predictive Values); "ValueDLR.Negative" (a value set for Negative Diagnostic Likelihood Ratio); "ValueDLR.Positive" (a value set for Positive Diagnostic Likelihood Ratio); "MinPvalue" (minimizes p-value associated with the statistical Chi-squared test which measures the association between the marker and the binary result obtained on using the cutpoint); "ObservedPrev" (The closest value to observed prevalence); "MeanPrev" (The closest value to the mean of the diagnostic test values); "PrevalenceMatching" (The value for which predicted prevalence is practically equal to observed prevalence).
`cutoffs_labels`	vector of user-supplied labels for the cutoffs. Must be a character vector of the same length as cutoffs_at.
`cutoffs_accuracy`	A numeric to specify the accuracy of the cutoff values to show.
`cutoffs_pt_size`	A numeric to specify the size of the cutoff points.
`cutoffs_pt_shape`	A numeric to specify the shape of the cutoff points.
`cutoffs_pt_stroke`	A numeric to specify the stroke of the cutoff points.
`cutoffs_labal_fg`	A character string to specify the color of the cutoff labels.
`cutoffs_label_size`	A numeric to specify the size of the cutoff labels.
`cutoffs_label_bg`	A character string to specify the background color of the cutoff labels.
`cutoffs_label_bg_r`	A numeric to specify the radius of the background of the cutoff labels.
`show_auc`	A character string to specify the position of the AUC values. "auto" (default): Automatically determine the position based on the plot. When there is a single group or 'facet_by' is provided, the AUC is placed on the plot. Otherwise, the AUC is placed in the legend. "none": Do not display the AUC values. "legend": Display the AUC values in the legend. "plot": Display the AUC values on the plot (left/right bottom corner).
`auc_accuracy`	A numeric to specify the accuracy of the AUC values.
`auc_size`	A numeric to specify the size of the AUC values when they are displayed on the plot.
`theme`	A character string or a theme class (i.e. ggplot2::theme_classic) specifying the theme to use. Default is "theme_this".
`theme_args`	A list of arguments to pass to the theme function.
`palette`	A character string specifying the palette to use. A named list or vector can be used to specify the palettes for different `split_by` values.
`palcolor`	A character string specifying the color to use in the palette. A named list can be used to specify the colors for different `split_by` values. If some values are missing, the values from the palette will be used (palcolor will be NULL for those values).
`alpha`	A numeric value specifying the transparency of the plot.
`facet_by`	A character string specifying the column name of the data frame to facet the plot. Otherwise, the data will be split by `split_by` and generate multiple plots and combine them into one using `patchwork::wrap_plots`
`facet_scales`	Whether to scale the axes of facets. Default is "fixed" Other options are "free", "free_x", "free_y". See `ggplot2::facet_wrap`
`facet_ncol`	A numeric value specifying the number of columns in the facet. When facet_by is a single column and facet_wrap is used.
`facet_nrow`	A numeric value specifying the number of rows in the facet. When facet_by is a single column and facet_wrap is used.
`facet_byrow`	A logical value indicating whether to fill the plots by row. Default is TRUE.
`aspect.ratio`	A numeric value specifying the aspect ratio of the plot.
`legend.position`	A character string specifying the position of the legend. if `waiver()`, for single groups, the legend will be "none", otherwise "right".
`legend.direction`	A character string specifying the direction of the legend.
`title`	A character string specifying the title of the plot. A function can be used to generate the title based on the default title. This is useful when split_by is used and the title needs to be dynamic.
`subtitle`	A character string specifying the subtitle of the plot.
`xlab`	A character string specifying the x-axis label.
`ylab`	A character string specifying the y-axis label.
`...`	Additional arguments.