Calculate the composition formed by applying all functions
Generate all possible pairwise mappings between the given multivariate
SCA projection using cocoreg interface
Computes the R^2 (variance explained) between two lists of data.frames
Create a path
Calculate the average of the composition formed by applying all functions
Mapping MASS::rlm
Compare data collections variable by variable
Mapping stats::lm
Rename variables of a data collection
Define a mapping function using pls::pcr
The Common Components by Regression (CoCoReg) algorith.
Circularly shift vector elements
Apply scale on a numeric data.frame
Generate cyclic paths
Catenate a list of data.frames to a matrix along dim
Compute "variance" of the matrices using Frobenius norm.
Combine a list of data.frames to a single molten data.frame
A rotation matrix
Generate all/some paths between points
Sum of squares
Apply fun to the bottom level of a nested list structure
Define a mapping function using glmnet::glmnet
Run BGFA by Klami et al using data format conventions of this repo
Return a specific variation component
Mapping svm
Project BGFA components common to all datasets back to the original space
A data collection with one unrelated dataset
Compute ds_variability for all datasets in a data collection
generate_mapping_function
Generate a mapping function between two datasets
Remove rows with NA values from a list of data.frames
Replicate matrix to create a larger one
Mapping svm using sigmoid
SVM using sigmoid kernel
Sum-of-squares values showing what portion of variance in dvec is explained
Contains functions to create synthetic datasets with different properties.
Scales variables in data.frame dfx using ordinary least squares such
Extract important properties of data collection
Make 2D gauss data (maybe obsolete)
Extract R2 values from a list of mappings using summary()
Melt data.frame into ggplottable format
Standard error of mean
A data collection with variables that "become unrelated during measurement"
Generate non-cyclic paths
Plotting data.frame using ggplot
PCA projection using cocoreg interface
COCOREG style analysis using RGCCA projection
Helper function to get the starting dataset based on
Apply extracted properties of a data collection to a data collection (restore)
Create multivariate synthetic data
Add notch-like gaussian snippets to an existing signal x
Catenate a set of data collections (lists of data.frames) into a single molted data.frame.
Catenate a list of data.frames vertically to a single data.frame
A dollection with unrelated variables
Compute D_joint for dataset i separately for all paths
A non-linear data collection using piecewise linearity
An illustrative synthetic datacollection
Mapping randomForest
Plot a list of data.frames using ggplot2
Append dataset names to variable names of the respective dataset
Compute RMSE between vectors v1 and v2
Compute "variance" of the vectors var()
Make vector of unit norm
Validate a data collection for use with cocoreg
Compute RMSE between data.matrices dm1 and dm2
Define a mapping function using MASS::lm.ridge
Compute variability_components for a dataset
Plotting data collections using ggplot
Compute total, within group and between group variability using fun
Apply GFA using the same interface as cocoreg()
Run scale() on a list of data.frames
Apply PCA to the data after catenating data.frames horizontally
Get [min, max] of a list of numeric objects
Compute Euclidean norm of vector
Reorders a nested list of ggplots
Add a data.frame (dataset) to a list of data.frames (datasets)
Determine the variability of matrices under row suffling