# Extract Shared Variation in Collections of Data Sets Using Regression Models

## Documentation for package ‘cocoreg’ version 0.1.1

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

add_notches | Add notch-like gaussian snippets to an existing signal x
apply_dc_meta | Apply extracted properties of a data collection to a data collection (restore)
average_R2_dflst | Computes the R^2 (variance explained) between two lists of data.frames
BGFA_cocoreg_interface | Apply GFA using the same interface as cocoreg()
BGFA_joint_info | Project BGFA components common to all datasets back to the original space
cocoreg | The Common Components by Regression (CoCoReg) algorithm
cocoreg_by_path | Compute D_joint for dataset i separately for all paths Can be used to study path variability
compose | Calculate the composition formed by applying all functions in the given path to a dataset.
compose_all | Calculate the average of the composition formed by applying all functions in all possible paths to a dataset.
create_mappings | Generate all possible pairwise mappings between the given multivariate datasets.
create_syndata_mv | Create multivariate synthetic data
create_syndata_pwl | A non-linear data collection using piecewise linearity
create_syn_data_puvar | A data collection with variables that "become unrelated during measurement"
create_syn_data_toy | An illustrative synthetic data collection
create_syn_data_uds | A data collection with one unrelated dataset
create_syn_data_uvar | A collection with unrelated variables
create_Z_linear | Contains functions to create synthetic datasets with different properties. The create_syn_data_*() functions follow the scheme: "total variation = shared_by_all + shared_by_subset + noise" Create signals
cshift | Circularly shift vector elements
data_collections2ggdf | Catenate a set of data collections (lists of data.frames) into a single molted data.frame.
data_matrix_rmse | Compute RMSE between data.matrices dm1 and dm2
dc_variability | Compute ds_variability for all datasets in a data collection
dflst2array | Catenate a list of data.frames to a matrix along dim
dflst2df | Catenate a list of data.frames vertically to a single data.frame
dflst2dfmelt | Combine a list of data.frames to a single molten data.frame
dflst_add_ds | Add a data.frame (dataset) to a list of data.frames (datasets)
dflst_dsnames2varnames | Append dataset names to variable names of the respective dataset
dflst_pca | Apply PCA to the data after catenating data.frames horizontally
df_ggplot_melt | Melt data.frame into ggplottable format
df_scale | Apply scale on a numeric data.frame
df_scale_ols | Scales variables in data.frame dfx using ordinary least squares such
dl_remove_NA | Remove rows with NA values from a list of data.frames
dl_scale | Run scale() on a list of data.frames
ds_variability | Compute variability_components for a dataset
generate_mapping_function | Generate a mapping function between two datasets
generate_paths | Generate all/some paths between points
generate_paths_cyclic | Generate cyclic paths
generate_paths_noncyclic | Generate non-cyclic paths
get_dc_meta | Extract important properties of data collection
get_range_datalist | Get [min, max] of a list of numeric objects
get_starting_dataset | Helper function to get the starting dataset based on a path.
ggcompare_dclst | Compare data collections variable by variable
ggplot_dclst | Plotting data collections using ggplot
ggplot_df | Plotting data.frame using ggplot
ggplot_dflst | Plot a list of data.frames using ggplot2
make_data_gauss_2d | Make 2D gauss data (maybe obsolete)
mappings_R2_matrix | Extract R2 values from a list of mappings using summary()
mapping_glmnet | Define a mapping function using glmnet::glmnet
mapping_lm | Mapping stats::lm
mapping_lmridge | Define a mapping function using MASS::lm.ridge
mapping_pcr | Define a mapping function using pls::pcr
mapping_rf | Mapping randomForest
mapping_rlm | Mapping MASS::rlm
mapping_svm | Mapping svm
mapping_svm_sigmoid | Mapping svm using sigmoid
matrix_variability | Compute "variance" of the matrices using Frobenius norm. Variance is by default computed with respect to the mean of the matrices.
nplst_reorder_grid | Reorders a nested list of ggplots
PCA_cocoreg_interface | PCA projection using cocoreg interface
rename_variables | Rename variables of a data collection
repmat | Replicate matrix to create a larger one
RGCCA_cocoreg_interface | COCOREG style analysis using RGCCA projection
rmse | Compute RMSE between vectors v1 and v2
rotation_matrix | A rotation matrix
row_suffle_variability | Determine the variability of matrices under row shuffling
SCA_cocoreg_interface | SCA projection using cocoreg interface
se | Standard error of mean
ss | Sum of squares
svm_sigmoid | SVM using sigmoid kernel
to_unit_vec | Make vector of unit norm
traverse_nested_list | Apply fun to the bottom level of a nested list structure
validate_data | Validate a data collection for use with cocoreg
variability_components | Compute total, within group and between group variability using fun
variation_shared_by | Return a specific variation component
var_explained | Sum-of-squares values showing what portion of variance in dvec is explained by dvec_est
vecnorm | Compute Euclidean norm of vector
vector_variability | Compute "variance" of the vectors var()
wrapper_BGFA | Run BGFA by Klami et. al. using data format conventions of this repo