plot_calibration_data {familiar}

Plot calibration figures.

Description

This method creates calibration plots from calibration data stored in a familiarCollection object. For this figures, the expected (predicted) values are plotted against the observed values. A well-calibrated model should be close to the identity line.

Usage

plot_calibration_data(
  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(),
  x_label_shared = "column",
  y_label = waiver(),
  y_label_shared = "row",
  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,
  show_density = TRUE,
  show_calibration_fit = TRUE,
  show_goodness_of_fit = TRUE,
  density_plot_height = grid::unit(1, "cm"),
  width = waiver(),
  height = waiver(),
  units = waiver(),
  export_collection = FALSE,
  ...
)

## S4 method for signature 'ANY'
plot_calibration_data(
  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(),
  x_label_shared = "column",
  y_label = waiver(),
  y_label_shared = "row",
  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,
  show_density = TRUE,
  show_calibration_fit = TRUE,
  show_goodness_of_fit = TRUE,
  density_plot_height = grid::unit(1, "cm"),
  width = waiver(),
  height = waiver(),
  units = waiver(),
  export_collection = FALSE,
  ...
)

## S4 method for signature 'familiarCollection'
plot_calibration_data(
  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(),
  x_label_shared = "column",
  y_label = waiver(),
  y_label_shared = "row",
  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,
  show_density = TRUE,
  show_calibration_fit = TRUE,
  show_goodness_of_fit = TRUE,
  density_plot_height = grid::unit(1, "cm"),
  width = waiver(),
  height = waiver(),
  units = waiver(),
  export_collection = FALSE,
  ...
)

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 calibration plots are saved to. Output is saved in the calibration subdirectory. If NULL no figures are saved, 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 data points and fit lines in case a non-singular variable was provided to the color_by argument.

x_label

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

x_label_shared

(optional) Sharing of x-axis labels between facets. One of three values:

• overall: A single label is placed at the bottom of the figure. Tick text (but not the ticks themselves) is removed for all but the bottom facet plot(s).

• column: A label is placed at the bottom of each column. Tick text (but not the ticks themselves) is removed for all but the bottom facet plot(s).

• individual: A label is placed below each facet plot. Tick text is kept.

y_label

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

y_label_shared

(optional) Sharing of y-axis labels between facets. One of three values:

• overall: A single label is placed to the left of the figure. Tick text (but not the ticks themselves) is removed for all but the left-most facet plot(s).

• row: A label is placed to the left of each row. Tick text (but not the ticks themselves) is removed for all but the left-most facet plot(s).

• individual: A label is placed below each facet plot. Tick text is kept.

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.

show_density

(optional) Show point density in top margin of the figure. If color_by is set, this information will not be shown.

show_calibration_fit

(optional) Specifies whether the calibration in the large and calibration slope are annotated in the plot. If color_by is set, this information will not be shown.

show_goodness_of_fit

(optional) Specifies whether a the results of goodness of fit tests are annotated in the plot. If color_by is set, this information will not be shown.

density_plot_height

(optional) Height of the density plot. The height is 1.5 cm by default. Height is expected to be grid unit (see grid::unit), which also allows for specifying relative heights. Will be ignored if show_density is FALSE.

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_calibration_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.

filename

File name to create on disk.

plot

Plot to save, defaults to last plot displayed.

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.

path

Path of the directory to save plot to: path and filename are combined to create the fully qualified file name. Defaults to the working directory.

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 a calibration plot for each model in each dataset. Any data used for calibration (e.g. baseline survival) is obtained during model creation.

Available splitting variables are: fs_method, learner, data_set and evaluation_time (survival analysis only) and positive_class (multinomial endpoints only). By default, separate figures are created for each combination of fs_method and learner, with facetting by data_set.

Calibration in survival analysis is performed at set time points so that survival probabilities can be computed from the model, and compared with observed survival probabilities. This is done differently depending on the underlying model. For Cox partial hazards regression models, the base survival (of the development samples) are used, whereas accelerated failure time models (e.g. Weibull) and survival random forests can be used to directly predict survival probabilities at a given time point. For survival analysis, evaluation_time is an additional facet variable (by default).

Calibration for multinomial endpoints is performed in a one-against-all manner. This yields calibration information for each individual class of the endpoint. For such endpoints, positive_class is an additional facet variable (by default).

Calibration plots have a density plot in the margin, which shows the density of the plotted points, ordered by the expected probability or value. For binomial and multinomial outcomes, the density for positive and negative classes are shown separately. Note that this information is only provided in when color_by is not used as a splitting variable (i.e. one calibration plot per facet).

Calibration plots are annotated with the intercept and the slope of a linear model fitted to the sample points. A well-calibrated model has an intercept close to 0.0 and a slope of 1.0. Intercept and slope are shown with their respective 95% confidence intervals. In addition, goodness-of-fit tests may be shown. For most endpoints these are based on the Hosmer-Lemeshow (HL) test, but for survival endpoints both the Nam-D'Agostino (ND) and the Greenwood-Nam-D'Agostino (GND) tests are shown. Note that this information is only annotated when color_by is not used as a splitting variable (i.e. one calibration plot per facet).

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.

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

Value

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

References

1. Hosmer, D. W., Hosmer, T., Le Cessie, S. & Lemeshow, S. A comparison of goodness-of-fit tests for the logistic regression model. Stat. Med. 16, 965–980 (1997).

2. D’Agostino, R. B. & Nam, B.-H. Evaluation of the Performance of Survival Analysis Models: Discrimination and Calibration Measures. in Handbook of Statistics vol. 23 1–25 (Elsevier, 2003).

3. Demler, O. V., Paynter, N. P. & Cook, N. R. Tests of calibration and goodness-of-fit in the survival setting. Stat. Med. 34, 1659–1680 (2015).