qiime longitudinal feature-volatility (version 2019.4)

--i-table: ARTIFACT FeatureTable[Frequency] Feature table containing all features that should be used for target prediction. [required]:

--p-state-column: TEXT Metadata containing collection time (state) values for each sample. Must contain exclusively numeric values. [required]:

--p-individual-id-column: TEXT Metadata column containing IDs for individual subjects. [optional]:

--p-cv: INTEGER Number of k-fold cross-validations to perform. Range(1, None) [default: 5]:

--p-random-state: INTEGER Seed used by random number generator. [optional]:

--p-n-estimators: INTEGER Range(1, None) Number of trees to grow for estimation. More trees will improve predictive accuracy up to a threshold level, but will also increase time and memory requirements. This parameter only affects ensemble estimators, such as Random Forest, AdaBoost, ExtraTrees, and GradientBoosting. [default: 100]:

--p-estimator: :

--p-parameter-tuning: --p-no-parameter-tuning Automatically tune hyperparameters using random grid search. [default: False]:

--p-missing-samples: :

--m-metadata-file [required]s

--m-metadata-file [required] 0

Feature volatility analysis

Identify features that are predictive of a numeric metadata column, state_column (e.g., time), and plot their relative frequencies across states using interactive feature volatility plots. A supervised learning regressor is used to identify important features and assess their ability to predict sample states. state_column will typically be a measure of time, but any numeric metadata column can be used.

Parameters

table : FeatureTable[Frequency]: Feature table containing all features that should be used for target prediction.
metadata : Metadata: Sample metadata file containing individual_id_column.
state_column : Str: Metadata containing collection time (state) values for each sample. Must contain exclusively numeric values.
individual_id_column : Str, optional: Metadata column containing IDs for individual subjects.
cv : Int % Range(1, None), optional: Number of k-fold cross-validations to perform.
random_state : Int, optional: Seed used by random number generator.
n_estimators : Int % Range(1, None), optional: Number of trees to grow for estimation. More trees will improve predictive accuracy up to a threshold level, but will also increase time and memory requirements. This parameter only affects ensemble estimators, such as Random Forest, AdaBoost, ExtraTrees, and GradientBoosting.
estimator : Str % Choices('RandomForestRegressor', 'ExtraTreesRegressor', 'GradientBoostingRegressor', 'AdaBoostRegressor', 'ElasticNet', 'Ridge', 'Lasso', 'KNeighborsRegressor', 'LinearSVR', 'SVR'), optional: Estimator method to use for sample prediction.
parameter_tuning : Bool, optional: Automatically tune hyperparameters using random grid search.
missing_samples : Str % Choices('error', 'ignore'), optional: How to handle missing samples in metadata. "error" will fail if missing samples are detected. "ignore" will cause the feature table and metadata to be filtered, so that only samples found in both files are retained.

Returns

filtered_table : FeatureTable[RelativeFrequency]: Feature table containing only important features.
feature_importance : FeatureData[Importance]: Importance of each input feature to model accuracy.
volatility_plot : Visualization: Interactive volatility plot visualization.
accuracy_results : Visualization: Accuracy results visualization.
sample_estimator : SampleEstimator[Regressor]: Trained sample regressor.