Data Integration with Two-Way Orthogonal Partial Least Squares

Documentation for package ‘OmicsPLS’ version 2.0.2

adjR2	Gridwise adjusted R2 for O2PLS
crossval_o2m	Cross-validate procedure for O2PLS
crossval_o2m_adjR2	Adjusted Cross-validate procedure for O2PLS
crossval_sparsity	Perform cross-validation to find the optimal number of variables/groups to keep for each joint component
impute_matrix	Impute missing values in a matrix
loadings	Extract the loadings from an O2PLS fit
loadings.o2m	Extract the loadings from an O2PLS fit
loocv	K fold CV for O2PLS
loocv_combi	K-fold CV based on symmetrized prediction error
mse	Calculate mean squared difference
norm_vec	Norm of a vector
o2m	Perform O2PLS data integration with two-way orthogonal corrections
OmicsPLS	Data integration with O2PLS: Two-Way Orthogonal Partial Least Squares
orth	Orthogonalize a matrix
orth_vec	Orthogonalize a sparse loading vector with regard to a matrix
plot.o2m	Plot one or two loading vectors for class o2m
predict.o2m	Predicts X or Y
print.cvo2m	Cross-validate procedure for O2PLS
print.o2m	Print function for O2PLS.
print.pre.o2m	Print function for O2PLS.
rmsep	Root MSE of Prediction
rmsep_combi	Symmetrized root MSE of Prediction
scores	Extract the scores from an O2PLS fit
scores.o2m	Extract the scores from an O2PLS fit
ssq	Calculate Sum of Squares
summary.o2m	Summary of an O2PLS fit
thresh_n	Soft threshholding a vector with respect to a number of variables
thresh_n_gr	Soft threshholding a vector with respect to a number of groups
vnorm	Norm of a vector or columns of a matrix