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familiar (version 1.5.0)

plot_decision_curve: Plot decision curves.

Description

This method creates decision curves based on data in a familiarCollection object.

Usage

plot_decision_curve(
  object,
  draw = FALSE,
  dir_path = NULL,
  split_by = NULL,
  color_by = NULL,
  facet_by = NULL,
  facet_wrap_cols = NULL,
  ggtheme = NULL,
  discrete_palette = NULL,
  x_label = waiver(),
  y_label = waiver(),
  legend_label = waiver(),
  plot_title = waiver(),
  plot_sub_title = waiver(),
  caption = NULL,
  x_range = NULL,
  x_n_breaks = 5,
  x_breaks = NULL,
  y_range = NULL,
  y_n_breaks = 5,
  y_breaks = NULL,
  conf_int_style = c("ribbon", "step", "none"),
  conf_int_alpha = 0.4,
  width = waiver(),
  height = waiver(),
  units = waiver(),
  export_collection = FALSE,
  ...
)
# S4 method for ANY
plot_decision_curve(
  object,
  draw = FALSE,
  dir_path = NULL,
  split_by = NULL,
  color_by = NULL,
  facet_by = NULL,
  facet_wrap_cols = NULL,
  ggtheme = NULL,
  discrete_palette = NULL,
  x_label = waiver(),
  y_label = waiver(),
  legend_label = waiver(),
  plot_title = waiver(),
  plot_sub_title = waiver(),
  caption = NULL,
  x_range = NULL,
  x_n_breaks = 5,
  x_breaks = NULL,
  y_range = NULL,
  y_n_breaks = 5,
  y_breaks = NULL,
  conf_int_style = c("ribbon", "step", "none"),
  conf_int_alpha = 0.4,
  width = waiver(),
  height = waiver(),
  units = waiver(),
  export_collection = FALSE,
  ...
)
# S4 method for familiarCollection
plot_decision_curve(
  object,
  draw = FALSE,
  dir_path = NULL,
  split_by = NULL,
  color_by = NULL,
  facet_by = NULL,
  facet_wrap_cols = NULL,
  ggtheme = NULL,
  discrete_palette = NULL,
  x_label = waiver(),
  y_label = waiver(),
  legend_label = waiver(),
  plot_title = waiver(),
  plot_sub_title = waiver(),
  caption = NULL,
  x_range = NULL,
  x_n_breaks = 5,
  x_breaks = NULL,
  y_range = NULL,
  y_n_breaks = 5,
  y_breaks = NULL,
  conf_int_style = c("ribbon", "step", "none"),
  conf_int_alpha = 0.4,
  width = waiver(),
  height = waiver(),
  units = waiver(),
  export_collection = FALSE,
  ...
)

Value

NULL or list of plot objects, if dir_path is NULL.

Arguments

object

familiarCollection object, or one or more familiarData objects, that will be internally converted to a familiarCollection object. It is also possible to provide a familiarEnsemble or one or more familiarModel objects together with the data from which data is computed prior to export. Paths to such files can also be provided.

draw

(optional) Draws the plot if TRUE.

dir_path

(optional) Path to the directory where created decision curve plots are saved to. Output is saved in the decision_curve_analysis subdirectory. If NULL, figures are written to the folder, but are returned instead.

split_by

(optional) Splitting variables. This refers to column names on which datasets are split. A separate figure is created for each split. See details for available variables.

color_by

(optional) Variables used to determine fill colour of plot objects. The variables cannot overlap with those provided to the split_by argument, but may overlap with other arguments. See details for available variables.

facet_by

(optional) Variables used to determine how and if facets of each figure appear. In case the facet_wrap_cols argument is NULL, the first variable is used to define columns, and the remaing variables are used to define rows of facets. The variables cannot overlap with those provided to the split_by argument, but may overlap with other arguments. See details for available variables.

facet_wrap_cols

(optional) Number of columns to generate when facet wrapping. If NULL, a facet grid is produced instead.

ggtheme

(optional) ggplot theme to use for plotting.

discrete_palette

(optional) Palette to use to color the different plot elements in case a value was provided to the color_by argument.

x_label

(optional) Label to provide to the x-axis. If NULL, no label is shown.

y_label

(optional) Label to provide to the y-axis. If NULL, no label is shown.

legend_label

(optional) Label to provide to the legend. If NULL, the legend will not have a name.

plot_title

(optional) Label to provide as figure title. If NULL, no title is shown.

plot_sub_title

(optional) Label to provide as figure subtitle. If NULL, no subtitle is shown.

caption

(optional) Label to provide as figure caption. If NULL, no caption is shown.

x_range

(optional) Value range for the x-axis.

x_n_breaks

(optional) Number of breaks to show on the x-axis of the plot. x_n_breaks is used to determine the x_breaks argument in case it is unset.

x_breaks

(optional) Break points on the x-axis of the plot.

y_range

(optional) Value range for the y-axis.

y_n_breaks

(optional) Number of breaks to show on the y-axis of the plot. y_n_breaks is used to determine the y_breaks argument in case it is unset.

y_breaks

(optional) Break points on the y-axis of the plot.

conf_int_style

(optional) Confidence interval style. See details for allowed styles.

conf_int_alpha

(optional) Alpha value to determine transparency of confidence intervals or, alternatively, other plot elements with which the confidence interval overlaps. Only values between 0.0 (fully transparent) and 1.0 (fully opaque) are allowed.

width

(optional) Width of the plot. A default value is derived from the number of facets.

height

(optional) Height of the plot. A default value is derived from the number of features and the number of facets.

units

(optional) Plot size unit. Either cm (default), mm or in.

export_collection

(optional) Exports the collection if TRUE.

...

Arguments passed on to as_familiar_collection, ggplot2::ggsave, extract_decision_curve_data

familiar_data_names

Names of the dataset(s). Only used if the object parameter is one or more familiarData objects.

collection_name

Name of the collection.

device

Device to use. Can either be a device function (e.g. png), or one of "eps", "ps", "tex" (pictex), "pdf", "jpeg", "tiff", "png", "bmp", "svg" or "wmf" (windows only). If NULL (default), the device is guessed based on the filename extension.

scale

Multiplicative scaling factor.

dpi

Plot resolution. Also accepts a string input: "retina" (320), "print" (300), or "screen" (72). Applies only to raster output types.

limitsize

When TRUE (the default), ggsave() will not save images larger than 50x50 inches, to prevent the common error of specifying dimensions in pixels.

bg

Background colour. If NULL, uses the plot.background fill value from the plot theme.

create.dir

Whether to create new directories if a non-existing directory is specified in the filename or path (TRUE) or return an error (FALSE, default). If FALSE and run in an interactive session, a prompt will appear asking to create a new directory when necessary.

data

A dataObject object, data.table or data.frame that constitutes the data that are assessed.

is_pre_processed

Flag that indicates whether the data was already pre-processed externally, e.g. normalised and clustered. Only used if the data argument is a data.table or data.frame.

cl

Cluster created using the parallel package. This cluster is then used to speed up computation through parallellisation.

evaluation_times

One or more time points that are used for in analysis of survival problems when data has to be assessed at a set time, e.g. calibration. If not provided explicitly, this parameter is read from settings used at creation of the underlying familiarModel objects. Only used for survival outcomes.

ensemble_method

Method for ensembling predictions from models for the same sample. Available methods are:

  • median (default): Use the median of the predicted values as the ensemble value for a sample.

  • mean: Use the mean of the predicted values as the ensemble value for a sample.

verbose

Flag to indicate whether feedback should be provided on the computation and extraction of various data elements.

message_indent

Number of indentation steps for messages shown during computation and extraction of various data elements.

detail_level

(optional) Sets the level at which results are computed and aggregated.

  • ensemble: Results are computed at the ensemble level, i.e. over all models in the ensemble. This means that, for example, bias-corrected estimates of model performance are assessed by creating (at least) 20 bootstraps and computing the model performance of the ensemble model for each bootstrap.

  • hybrid (default): Results are computed at the level of models in an ensemble. This means that, for example, bias-corrected estimates of model performance are directly computed using the models in the ensemble. If there are at least 20 trained models in the ensemble, performance is computed for each model, in contrast to ensemble where performance is computed for the ensemble of models. If there are less than 20 trained models in the ensemble, bootstraps are created so that at least 20 point estimates can be made.

  • model: Results are computed at the model level. This means that, for example, bias-corrected estimates of model performance are assessed by creating (at least) 20 bootstraps and computing the performance of the model for each bootstrap.

Note that each level of detail has a different interpretation for bootstrap confidence intervals. For ensemble and model these are the confidence intervals for the ensemble and an individual model, respectively. That is, the confidence interval describes the range where an estimate produced by a respective ensemble or model trained on a repeat of the experiment may be found with the probability of the confidence level. For hybrid, it represents the range where any single model trained on a repeat of the experiment may be found with the probability of the confidence level. By definition, confidence intervals obtained using hybrid are at least as wide as those for ensemble. hybrid offers the correct interpretation if the goal of the analysis is to assess the result of a single, unspecified, model.

hybrid is generally computationally less expensive then ensemble, which in turn is somewhat less expensive than model.

A non-default detail_level parameter can be specified for separate evaluation steps by providing a parameter value in a named list with data elements, e.g. list("auc_data"="ensemble", "model_performance"="hybrid"). This parameter can be set for the following data elements: auc_data, decision_curve_analyis, model_performance, permutation_vimp, ice_data, prediction_data and confusion_matrix.

estimation_type

(optional) Sets the type of estimation that should be possible. This has the following options:

  • point: Point estimates.

  • bias_correction or bc: Bias-corrected estimates. A bias-corrected estimate is computed from (at least) 20 point estimates, and familiar may bootstrap the data to create them.

  • bootstrap_confidence_interval or bci (default): Bias-corrected estimates with bootstrap confidence intervals (Efron and Hastie, 2016). The number of point estimates required depends on the confidence_level parameter, and familiar may bootstrap the data to create them.

As with detail_level, a non-default estimation_type parameter can be specified for separate evaluation steps by providing a parameter value in a named list with data elements, e.g. list("auc_data"="bci", "model_performance"="point"). This parameter can be set for the following data elements: auc_data, decision_curve_analyis, model_performance, permutation_vimp, ice_data, and prediction_data.

aggregate_results

(optional) Flag that signifies whether results should be aggregated during evaluation. If estimation_type is bias_correction or bc, aggregation leads to a single bias-corrected estimate. If estimation_type is bootstrap_confidence_interval or bci, aggregation leads to a single bias-corrected estimate with lower and upper boundaries of the confidence interval. This has no effect if estimation_type is point.

The default value is equal to TRUE except when assessing metrics to assess model performance, as the default violin plot requires underlying data.

As with detail_level and estimation_type, a non-default aggregate_results parameter can be specified for separate evaluation steps by providing a parameter value in a named list with data elements, e.g. list("auc_data"=TRUE, , "model_performance"=FALSE). This parameter exists for the same elements as estimation_type.

confidence_level

(optional) Numeric value for the level at which confidence intervals are determined. In the case bootstraps are used to determine the confidence intervals bootstrap estimation, familiar uses the rule of thumb \(n = 20 / ci.level\) to determine the number of required bootstraps.

The default value is 0.95.

bootstrap_ci_method

(optional) Method used to determine bootstrap confidence intervals (Efron and Hastie, 2016). The following methods are implemented:

  • percentile (default): Confidence intervals obtained using the percentile method.

  • bc: Bias-corrected confidence intervals.

Note that the standard method is not implemented because this method is often not suitable due to non-normal distributions. The bias-corrected and accelerated (BCa) method is not implemented yet.

Details

This function generates plots for decision curves.

Available splitting variables are: fs_method, learner, data_set and positive_class (categorical outcomes) or evaluation_time (survival outcomes). By default, the data is split by fs_method and learner, with faceting by data_set and colouring by positive_class or evaluation_time.

Available palettes for discrete_palette are those listed by grDevices::palette.pals() (requires R >= 4.0.0), grDevices::hcl.pals() (requires R >= 3.6.0) and rainbow, heat.colors, terrain.colors, topo.colors and cm.colors, which correspond to the palettes of the same name in grDevices. If not specified, a default palette based on palettes in Tableau are used. You may also specify your own palette by using colour names listed by grDevices::colors() or through hexadecimal RGB strings.

Bootstrap confidence intervals of the decision curve (if present) can be shown using various styles set by conf_int_style:

  • ribbon (default): confidence intervals are shown as a ribbon with an opacity of conf_int_alpha around the point estimate of the decision curve.

  • step (default): confidence intervals are shown as a step function around the point estimate of the decision curve.

  • none: confidence intervals are not shown. The point estimate of the decision curve is shown as usual.

Labelling methods such as set_fs_method_names or set_data_set_names can be applied to the familiarCollection object to update labels, and order the output in the figure.

References

  1. Vickers, A. J. & Elkin, E. B. Decision curve analysis: a novel method for evaluating prediction models. Med. Decis. Making 26, 565–574 (2006).

  2. Vickers, A. J., Cronin, A. M., Elkin, E. B. & Gonen, M. Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers. BMC Med. Inform. Decis. Mak. 8, 53 (2008).

  3. Vickers, A. J., van Calster, B. & Steyerberg, E. W. A simple, step-by-step guide to interpreting decision curve analysis. Diagn Progn Res 3, 18 (2019).