Canonical Joint and Individual Variation Explained (CJIVE)


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Documentation for package ‘CJIVE’ version 0.1.0

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AdjSigVarExp Adjust Signal Variation Explained
cc.jive Canonical (Correlation) JIVE
cc.jive.pred CJIVE joint subject score prediction
chord.norm.diff Chordal norm between column-subspaces of two matrices
ConvSims_gg Convert simulation study results
create.graph.long Function for plotting networks with ggplot
GenerateToyData Generate 'Toy' Data
GetSimResults_Dir Retrieve simulation results
gg.corr.plot Function for plotting Pearson correlations between predicted and true subject scores within the simulation study described in CJIVE manuscript
gg.load.norm.plot Function for plotting chordal norms between estimated and true variable loading subspaces within the simulation study described in CJIVE manuscript
gg.norm.plot Function for plotting chordal norms between estimated and true subspaces within the simulation study described in CJIVE manuscript
gg.rank.plot Function for plotting selected joint ranks
gg.score.norm.plot Function for plotting chordal norms between estimated and true subject score subspaces within the simulation study described in CJIVE manuscript
MatVar Matrix variation (i.e. Frobenius norm)
MatVar2 Alternative calculation - Matrix variation (i.e. Frobenius norm)
Melt.Sim.Cors Converts correlations of predicted to true joint subject scores to a format conducive to ggplot2
perm.jntrank Permutation Test for Joint Rank in CJIVE
scale_loadings Scale and sign-correct variable loadings to assist interpretation
show.image.2 Display a heatmap of a matrix (adapted from Erick Lock's show.image function in the r.jive package)
sjive Simple JIVE
vec2net.l Convert vector to network
vec2net.u Convert vector to network