R: Plot permutation variable importance.

plot_permutation_variable_importance {familiar}

R Documentation

Plot permutation variable importance.

Description

This function plots the data on permutation variable importance stored in a familiarCollection object.

Usage

plot_permutation_variable_importance(
  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 = "feature",
  legend_label = waiver(),
  plot_title = waiver(),
  plot_sub_title = waiver(),
  caption = NULL,
  x_range = NULL,
  x_n_breaks = 5,
  x_breaks = NULL,
  conf_int_style = c("point_line", "line", "bar_line", "none"),
  conf_int_alpha = 0.4,
  width = waiver(),
  height = waiver(),
  units = waiver(),
  export_collection = FALSE,
  ...
)

## S4 method for signature 'ANY'
plot_permutation_variable_importance(
  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 = "feature",
  legend_label = waiver(),
  plot_title = waiver(),
  plot_sub_title = waiver(),
  caption = NULL,
  x_range = NULL,
  x_n_breaks = 5,
  x_breaks = NULL,
  conf_int_style = c("point_line", "line", "bar_line", "none"),
  conf_int_alpha = 0.4,
  width = waiver(),
  height = waiver(),
  units = waiver(),
  export_collection = FALSE,
  ...
)

## S4 method for signature 'familiarCollection'
plot_permutation_variable_importance(
  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 = "feature",
  legend_label = waiver(),
  plot_title = waiver(),
  plot_sub_title = waiver(),
  caption = NULL,
  x_range = NULL,
  x_n_breaks = 5,
  x_breaks = NULL,
  conf_int_style = c("point_line", "line", "bar_line", "none"),
  conf_int_alpha = 0.4,
  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 figures are saved to. Output is saved in the `variable_importance` 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 used to fill the bars 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.
`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.
`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_permutation_vimp` `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. `metric` One or more metrics for assessing model performance. See the vignette on performance metrics for the available metrics. If not provided explicitly, this parameter is read from settings used at creation of the underlying `familiarModel` objects. `feature_cluster_method` The method used to perform clustering. These are the same methods as for the `cluster_method` configuration parameter: `none`, `hclust`, `agnes`, `diana` and `pam`. `none` cannot be used when extracting data regarding mutual correlation or feature expressions. If not provided explicitly, this parameter is read from settings used at creation of the underlying `familiarModel` objects. `feature_linkage_method` The method used for agglomerative clustering in `hclust` and `agnes`. These are the same methods as for the `cluster_linkage_method` configuration parameter: `average`, `single`, `complete`, `weighted`, and `ward`. If not provided explicitly, this parameter is read from settings used at creation of the underlying `familiarModel` objects. `feature_cluster_cut_method` The method used to divide features into separate clusters. The available methods are the same as for the `cluster_cut_method` configuration parameter: `silhouette`, `fixed_cut` and `dynamic_cut`. `silhouette` is available for all cluster methods, but `fixed_cut` only applies to methods that create hierarchical trees (`hclust`, `agnes` and `diana`). `dynamic_cut` requires the `dynamicTreeCut` package and can only be used with `agnes` and `hclust`. If not provided explicitly, this parameter is read from settings used at creation of the underlying `familiarModel` objects. `feature_similarity_threshold` The threshold level for pair-wise similarity that is required to form feature clusters with the `fixed_cut` method. If not provided explicitly, this parameter is read from settings used at creation of the underlying `familiarModel` objects. `feature_similarity_metric` Metric to determine pairwise similarity between features. Similarity is computed in the same manner as for clustering, and `feature_similarity_metric` therefore has the same options as `cluster_similarity_metric`: `mcfadden_r2`, `cox_snell_r2`, `nagelkerke_r2`, `spearman`, `kendall` and `pearson`. If not provided explicitly, this parameter is read from settings used at creation of the underlying `familiarModel` objects. `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 horizontal barplot that lists features by the estimated model improvement over that of a dataset where the respective feature is randomly permuted.

The following splitting variables are available for split_by, color_by and facet_by:

fs_method: feature selection methods.
learner: learners.
data_set: data sets.
metric: the model performance metrics.
evaluation_time: the evaluation times (survival outcomes only).
similarity_threshold: the similarity threshold used to identify groups of features to permute simultaneously.

By default, the data is split by fs_method, learner and metric, faceted by data_set and evaluation_time, and coloured by similarity_threshold.

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.

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

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

point_line (default): confidence intervals are shown as lines, on which the point estimate is likewise shown.
line (default): confidence intervals are shown as lines, but the point estimate is not shown.
bar_line: confidence intervals are shown as lines, with the point estimate shown as a bar plot with the opacity of conf_int_alpha.
none: confidence intervals are not shown. The point estimate is shown as a bar plot.

For metrics where lower values indicate better model performance, more negative permutation variable importance values indicate features that are more important. Because this may cause confusion, values obtained for these metrics are mirrored around 0.0 for plotting (but not any tabular data export).

Value

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

[Package familiar version 1.4.8 Index]