Parallel Factor Analysis Modelling of Longitudinal Microbiome Data
Create randomly initialized models to determine the correct number of ...
Bootstrapping procedure to determine PARAFAC model stability for a giv...
Calculate Factor Match Score for all initialized models.
Calculate sparsity across the feature mode of a multi-way array.
Calculate the variation explained by a PARAFAC model.
Calculate the variance explained of a PARAFAC model, per component
Core Consistency Diagnostic (CORCONDIA) calculation
Vectorize Fac object
Sign flip the loadings of many randomly initialized models to make con...
Import MicrobiotaProcess object for PARAFAC modelling
Import Phyloseq object for PARAFAC modelling
Import TreeSummarizedExperiment object for PARAFAC modelling
Initialize PARAFAC algorithm input vectors
Center a multi-way array
Perform a centered log-ratio transform over a multi-way array
Scale a multi-way array
Internal PARAFAC alternating least-squares (ALS) core algorithm
PARAFAC loss function calculation
Calculate gradient of PARAFAC model.
Parallel Factor Analysis
parafac4microbiome: Parallel Factor Analysis Modelling of Longitudinal...
Pipe operator
Plot diagnostics of many initialized PARAFAC models.
Plot a summary of the loadings of many initialized parafac models.
Plots Tucker Congruence Coefficients of randomly initialized models.
Plot a PARAFAC model
Process a multi-way array of count data.
Calculate Xhat from a model Fac object
Create a tensor out of a set of matrices similar to a component model.
Sort PARAFAC components based on variance explained per component.
Transform PARAFAC loadings to an orthonormal basis. Note: this functio...
Convert vectorized output of PARAFAC to a Fac list object with all loa...
Creation and selection of PARAllel FACtor Analysis (PARAFAC) models of longitudinal microbiome data. You can import your own data with our import functions or use one of the example datasets to create your own PARAFAC models. Selection of the optimal number of components can be done using assessModelQuality() and assessModelStability(). The selected model can then be plotted using plotPARAFACmodel(). The Parallel Factor Analysis method was originally described by Caroll and Chang (1970) <doi:10.1007/BF02310791> and Harshman (1970) <https://www.psychology.uwo.ca/faculty/harshman/wpppfac0.pdf>.
Useful links