Reference

Main Functions Functions that provide the main workflow of the package.
check.associations()	Calculate associations between features and labels
filter.features()	Perform unsupervised feature filtering.
create.data.split()	Split a dataset into training and a test sets.
normalize.features()	Perform feature normalization
train.model()	Model training
make.predictions()	Make predictions on a test set
evaluate.predictions()	Evaluate prediction results
Plots Functions to produce the major visual output, i.e. the model evaluation and model interpretation plot
check.confounders()	Check for potential confounders in the metadata
association.plot()	Visualize associations between features and classes
volcano.plot()	Visualize associations between features and classes as volcano plot
model.evaluation.plot()	Model Evaluation Plot
model.interpretation.plot()	Model Interpretation Plot
Miscellaneous Other functions for general data manipulation (some of them are probably mostly for internal use)
summarize.features()	Summarize features
filter(<label>)	Filter the label of a SIMACAT object
select.samples()	Select samples based on metadata
add.meta.pred()	Add metadata as predictors
create.label()	Create a label list
validate.data()	Validate samples in labels, features, and metadata
read.label()	Read label file
SIAMCAT class The SIAMCAT class and the constructor function
siamcat-class	The S4 SIAMCAT class
siamcat()	SIAMCAT constructor function
SIAMCAT SIAMCAT-package	SIAMCAT: Statistical Inference of Associations between Microbial Communities And host phenoTypes
Accessor functions Functions to retrieve information out of the SIAMCAT object
label()	Retrieve the label from a SIAMCAT object
meta()	Retrieve the metadata from a SIAMCAT object
get.orig_feat.matrix()	Retrieve the original features from a SIAMCAT object
associations()	Retrieve the results of association testing from a SIAMCAT object
assoc_param()	Retrieve the list of parameters for association testing from a SIAMCAT object
filt_params()	Retrieve the list of parameters for feature filtering from a SIAMCAT object
get.filt_feat.matrix()	Retrieve the filtered features from a SIAMCAT object
data_split()	Retrieve the data split from a SIAMCAT object
norm_params()	Retrieve the list of parameters for feature normalization from a SIAMCAT object
get.norm_feat.matrix()	Retrieve the normalized features from a SIAMCAT object
models()	Retrieve list of trained models from a SIAMCAT object
model_type()	Retrieve the machine learning method from a SIAMCAT object
feature_type()	Retrieve the feature type used for model training from a SIAMCAT object
feature_weights()	Retrieve the matrix of feature weights from a SIAMCAT object
weight_matrix()	Retrieve the weight matrix from a SIAMCAT object
pred_matrix()	Retrieve the prediction matrix from a SIAMCAT object
eval_data()	Retrieve the evaluation metrics from a SIAMCAT object
Included data Data included in the package
feat.crc.zeller	Example feature matrix
meta.crc.zeller	Example metadata matrix
siamcat_example	SIAMCAT example