export_feature_similarity-methods: Extract and export mutual correlation between features.

In familiar: End-to-End Automated Machine Learning and Model Evaluation

Extract and export mutual correlation between features.

Description

Extract and export mutual correlation between features in a familiarCollection.

Usage

export_feature_similarity(
  object,
  dir_path = NULL,
  aggregate_results = TRUE,
  features = waiver(),
  feature_cluster_method = waiver(),
  feature_linkage_method = waiver(),
  feature_cluster_cut_method = waiver(),
  feature_similarity_threshold = waiver(),
  export_dendrogram = FALSE,
  export_ordered_data = FALSE,
  export_clustering = FALSE,
  export_collection = FALSE,
  ...
)

## S4 method for signature 'familiarCollection'
export_feature_similarity(
  object,
  dir_path = NULL,
  aggregate_results = TRUE,
  features = waiver(),
  feature_cluster_method = waiver(),
  feature_linkage_method = waiver(),
  feature_cluster_cut_method = waiver(),
  feature_similarity_threshold = waiver(),
  export_dendrogram = FALSE,
  export_ordered_data = FALSE,
  export_clustering = FALSE,
  export_collection = FALSE,
  ...
)

## S4 method for signature 'ANY'
export_feature_similarity(
  object,
  dir_path = NULL,
  aggregate_results = TRUE,
  features = waiver(),
  feature_cluster_method = waiver(),
  feature_linkage_method = waiver(),
  feature_cluster_cut_method = waiver(),
  feature_similarity_threshold = waiver(),
  export_dendrogram = FALSE,
  export_ordered_data = FALSE,
  export_clustering = FALSE,
  export_collection = FALSE,
  ...
)

Arguments

object	A familiarCollection object, or other other objects from which a familiarCollection can be extracted. See details for more information.
dir_path	Path to folder where extracted data should be saved. NULL will allow export as a structured list of data.tables.
aggregate_results	Flag that signifies whether results should be aggregated for export.
features	Features that should be considered for extracting information from. Typically called in external workflows, e.g. for plotting. Internally, i.e. from summon_familiar, this variable is not used.
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.
export_dendrogram	Add dendrogram in the data element objects.
export_ordered_data	Add feature label ordering to data in the data element objects.
export_clustering	Add clustering information to data.
export_collection	(optional) Exports the collection if TRUE.
...	Arguments passed on to as_familiar_collection, as_data_object 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. data A data.frame or data.table, a path to such tables on a local or network drive, or a path to tabular data that may be converted to these formats. check_stringency Specifies stringency of various checks. This is mostly: strict: default value used for summon_familiar. Thoroughly checks input data. Used internally for checking development data. external_warn: value used for extract_data and related methods. Less stringent checks, but will warn for possible issues. Used internally for checking data for evaluation and explanation. external: value used for external methods such as predict. Less stringent checks, particularly for identifier and outcome columns, which may be completely absent. Used internally for predict. .no_features_required Internal flag to signify that data without features is allowed. Default: FALSE (most processing steps require features). batch_id_column (recommended) Name of the column containing batch or cohort identifiers. This parameter is required if more than one dataset is provided, or if external validation is performed. In familiar any row of data is organised by four identifiers: The batch identifier batch_id_column: This denotes the group to which a set of samples belongs, e.g. patients from a single study, samples measured in a batch, etc. The batch identifier is used for batch normalisation, as well as selection of development and validation datasets. The sample identifier sample_id_column: This denotes the sample level, e.g. data from a single individual. Subsets of data, e.g. bootstraps or cross-validation folds, are created at this level. The series identifier series_id_column: Indicates measurements on a single sample that may not share the same outcome value, e.g. a time series, or the number of cells in a view. The repetition identifier: Indicates repeated measurements in a single series where any feature values may differ, but the outcome does not. Repetition identifiers are always implicitly set when multiple entries for the same series of the same sample in the same batch that share the same outcome are encountered. sample_id_column (recommended) Name of the column containing sample or subject identifiers. See batch_id_column above for more details. If unset, every row will be identified as a single sample. series_id_column (optional) Name of the column containing series identifiers, which distinguish between measurements that are part of a series for a single sample. See batch_id_column above for more details. If unset, rows which share the same batch and sample identifiers but have a different outcome are assigned unique series identifiers. development_batch_id (optional) One or more batch or cohort identifiers to constitute data sets for development. Defaults to all, or all minus the identifiers in validation_batch_id for external validation. Required if external validation is performed and validation_batch_id is not provided. validation_batch_id (optional) One or more batch or cohort identifiers to constitute data sets for external validation. Defaults to all data sets except those in development_batch_id for external validation, or none if not. Required if development_batch_id is not provided. outcome_name (optional) Name of the modelled outcome. This name will be used in figures created by familiar. If not set, the column name in outcome_column will be used for binomial, multinomial, and continuous outcomes. For other outcomes (survival and competing_risk) no default is used. outcome_column (recommended) Name of the column containing the outcome of interest. May be identified from a formula, if a formula is provided as an argument. Otherwise an error is raised. Note that survival and competing_risk outcome type outcomes require two columns that indicate the time-to-event or the time of last follow-up and the event status. outcome_type (recommended) Type of outcome found in the outcome column. The outcome type determines many aspects of the overall process, e.g. the available variable importance methods and learners, but also the type of assessments that can be conducted to evaluate the resulting models. Implemented outcome types are: binomial: categorical outcome with 2 levels. multinomial: categorical outcome with 2 or more levels. continuous: general continuous numeric outcomes. survival: survival outcome for time-to-event data. If not provided, the algorithm will attempt to obtain outcome_type from contents of the outcome column. This may lead to unexpected results, and we therefore advise to provide this information manually. Note that competing_risk survival analysis are not fully supported, and is currently not a valid choice for outcome_type. The count outcome type was deprecated in version 2.0.0, and superseded by continuous. class_levels (optional) Class levels for binomial or multinomial outcomes. This argument can be used to specify the ordering of levels for categorical outcomes. These class levels must exactly match the levels present in the outcome column. event_indicator (recommended) Indicator for events in survival and competing_risk analyses. familiar will automatically recognise 1, true, t, y and yes as event indicators, including different capitalisations. If this parameter is set, it replaces the default values. censoring_indicator (recommended) Indicator for right-censoring in survival and competing_risk analyses. familiar will automatically recognise 0, false, f, n, no as censoring indicators, including different capitalisations. If this parameter is set, it replaces the default values. competing_risk_indicator (recommended) Indicator for competing risks in competing_risk analyses. There are no default values, and if unset, all values other than those specified by the event_indicator and censoring_indicator parameters are considered to indicate competing risks. exclude_features (optional) Feature columns that will be removed from the data set. Cannot overlap with features in signature, novelty_features or include_features. include_features (optional) Feature columns that are specifically included in the data set. By default all features are included. Cannot overlap with exclude_features, but may overlap signature. Features in signature and novelty_features are always included. If both exclude_features and include_features are provided, include_features takes precedence, provided that there is no overlap between the two. reference_method (optional) Method used to set reference levels for categorical features. There are several options: auto (default): Categorical features that are not explicitly set by the user, i.e. columns containing boolean values or characters, use the most frequent level as reference. Categorical features that are explicitly set, i.e. as factors, are used as is. always: Both automatically detected and user-specified categorical features have the reference level set to the most frequent level. Ordinal features are not altered, but are used as is. never: User-specified categorical features are used as is. Automatically detected categorical features are simply sorted, and the first level is then used as the reference level. This was the behaviour prior to familiar version 1.3.0.

Details

All parameters aside from object and dir_path are only used if object is not a familiarCollection object, or a path to one.

Feature similarity data can be created from dataObject, or data.table objects. For data.table, see as_data_object for additional arguments.

Value

A list containing a data.table (if dir_path is not provided), or nothing, as all data is exported to csv files.

familiar documentation built on May 23, 2026, 1:07 a.m.