Omics Data Integration Project

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Documentation for package ‘mixOmics’ version 6.3.1

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A B C D E G I K L M N P R S T U V W Y

-- A --

auroc Area Under the Curve (AUC) and Receiver Operating Characteristic (ROC) curves for supervised classification
auroc.mint.plsda Area Under the Curve (AUC) and Receiver Operating Characteristic (ROC) curves for supervised classification
auroc.mint.splsda Area Under the Curve (AUC) and Receiver Operating Characteristic (ROC) curves for supervised classification
auroc.plsda Area Under the Curve (AUC) and Receiver Operating Characteristic (ROC) curves for supervised classification
auroc.sgccda Area Under the Curve (AUC) and Receiver Operating Characteristic (ROC) curves for supervised classification
auroc.splsda Area Under the Curve (AUC) and Receiver Operating Characteristic (ROC) curves for supervised classification

-- B --

background.predict Calculate prediction areas
block.pls N-integration with Projection to Latent Structures models (PLS)
block.plsda N-integration with Projection to Latent Structures models (PLS) with Discriminant Analysis
block.spls N-integration and feature selection with sparse Projection to Latent Structures models (sPLS)
block.splsda N-integration and feature selection with Projection to Latent Structures models (PLS) with sparse Discriminant Analysis
breast.TCGA Breast Cancer multi omics data from TCGA
breast.tumors Human Breast Tumors Data

-- C --

cim Clustered Image Maps (CIMs) ("heat maps")
cimDiablo Clustered Image Maps (CIMs) ("heat maps") for DIABLO
circosPlot circosPlot for DIABLO
color.GreenRed Color Palette for mixOmics
color.jet Color Palette for mixOmics
color.mixo Color Palette for mixOmics
color.spectral Color Palette for mixOmics

-- D --

diverse.16S 16S microbiome data: most diverse bodysites from HMP

-- E --

estim.regul Estimate the parameters of regularization for Regularized CCA
estim.regul.default Estimate the parameters of regularization for Regularized CCA
explained_variance Calculation of explained variance

-- G --

get.BER Create confusion table and calculate the Balanced Error Rate
get.confusion_matrix Create confusion table and calculate the Balanced Error Rate

-- I --

image.estim.regul Plot the cross-validation score.
image.tune.rcc Plot the cross-validation score.
imgCor Image Maps of Correlation Matrices between two Data Sets
ipca Independent Principal Component Analysis

-- K --

Koren.16S 16S microbiome atherosclerosis study

-- L --

linnerud Linnerud Dataset
liver.toxicity Liver Toxicity Data
logratio.transfo Log-ratio transformation

-- M --

map Classification given Probabilities
mat.rank Matrix Rank
mint.block.pls NP-integration
mint.block.plsda NP-integration with Discriminant Analysis
mint.block.spls NP-integration for integration with variable selection
mint.block.splsda NP-integration with Discriminant Analysis and variable selection
mint.pca P-integration with Principal Component Analysis
mint.pls P-integration
mint.plsda P-integration with Projection to Latent Structures models (PLS) with Discriminant Analysis
mint.spls P-integration with variable selection
mint.splsda P-integration with Discriminant Analysis and variable selection
mixOmics PLS-derived methods: one function to rule them all!
multidrug Multidrug Resistence Data

-- N --

nearZeroVar Identification of zero- or near-zero variance predictors
network Relevance Network for (r)CCA and (s)PLS regression
network.default Relevance Network for (r)CCA and (s)PLS regression
network.pls Relevance Network for (r)CCA and (s)PLS regression
network.rcc Relevance Network for (r)CCA and (s)PLS regression
network.spls Relevance Network for (r)CCA and (s)PLS regression
nipals Non-linear Iterative Partial Least Squares (NIPALS) algorithm
nutrimouse Nutrimouse Dataset

-- P --

pca Principal Components Analysis
pcatune Tune the number of principal components in PCA
perf Compute evaluation criteria for PLS, sPLS, PLS-DA, sPLS-DA, MINT and DIABLO
perf.mint.splsda Compute evaluation criteria for PLS, sPLS, PLS-DA, sPLS-DA, MINT and DIABLO
perf.pls Compute evaluation criteria for PLS, sPLS, PLS-DA, sPLS-DA, MINT and DIABLO
perf.plsda Compute evaluation criteria for PLS, sPLS, PLS-DA, sPLS-DA, MINT and DIABLO
perf.sgccda Compute evaluation criteria for PLS, sPLS, PLS-DA, sPLS-DA, MINT and DIABLO
perf.spls Compute evaluation criteria for PLS, sPLS, PLS-DA, sPLS-DA, MINT and DIABLO
perf.splsda Compute evaluation criteria for PLS, sPLS, PLS-DA, sPLS-DA, MINT and DIABLO
plot.perf Plot for model performance
plot.perf.mint.plsda.mthd Plot for model performance
plot.perf.mint.splsda.mthd Plot for model performance
plot.perf.pls.mthd Plot for model performance
plot.perf.plsda.mthd Plot for model performance
plot.perf.sgccda.mthd Plot for model performance
plot.perf.spls.mthd Plot for model performance
plot.perf.splsda.mthd Plot for model performance
plot.rcc Canonical Correlations Plot
plot.tune Plot for model performance
plot.tune.block.splsda Plot for model performance
plot.tune.splsda Plot for model performance
plotArrow Arrow sample plot
plotDiablo Graphical output for the DIABLO framework
plotIndiv Plot of Individuals (Experimental Units)
plotIndiv.mint.spls Plot of Individuals (Experimental Units)
plotIndiv.mint.splsda Plot of Individuals (Experimental Units)
plotIndiv.pca Plot of Individuals (Experimental Units)
plotIndiv.pls Plot of Individuals (Experimental Units)
plotIndiv.rcc Plot of Individuals (Experimental Units)
plotIndiv.rgcca Plot of Individuals (Experimental Units)
plotIndiv.sgcca Plot of Individuals (Experimental Units)
plotIndiv.sipca Plot of Individuals (Experimental Units)
plotIndiv.spls Plot of Individuals (Experimental Units)
plotLoadings Plot of Loading vectors
plotLoadings.block.pls Plot of Loading vectors
plotLoadings.block.plsda Plot of Loading vectors
plotLoadings.block.spls Plot of Loading vectors
plotLoadings.block.splsda Plot of Loading vectors
plotLoadings.mint.pls Plot of Loading vectors
plotLoadings.mint.plsda Plot of Loading vectors
plotLoadings.mint.spls Plot of Loading vectors
plotLoadings.mint.splsda Plot of Loading vectors
plotLoadings.pca Plot of Loading vectors
plotLoadings.pls Plot of Loading vectors
plotLoadings.plsda Plot of Loading vectors
plotLoadings.rcc Plot of Loading vectors
plotLoadings.rgcca Plot of Loading vectors
plotLoadings.sgcca Plot of Loading vectors
plotLoadings.sgccda Plot of Loading vectors
plotLoadings.spls Plot of Loading vectors
plotLoadings.splsda Plot of Loading vectors
plotVar Plot of Variables
plotVar.pca Plot of Variables
plotVar.pls Plot of Variables
plotVar.plsda Plot of Variables
plotVar.rcc Plot of Variables
plotVar.rgcca Plot of Variables
plotVar.sgcca Plot of Variables
plotVar.spca Plot of Variables
plotVar.spls Plot of Variables
plotVar.splsda Plot of Variables
pls Partial Least Squares (PLS) Regression
plsda Partial Least Squares Discriminant Analysis (PLS-DA).
predict.mint.block.pls Predict Method for (mint).(block).(s)pls(da) methods
predict.mint.block.plsda Predict Method for (mint).(block).(s)pls(da) methods
predict.mint.block.spls Predict Method for (mint).(block).(s)pls(da) methods
predict.mint.block.splsda Predict Method for (mint).(block).(s)pls(da) methods
predict.mint.pls Predict Method for (mint).(block).(s)pls(da) methods
predict.mint.plsda Predict Method for (mint).(block).(s)pls(da) methods
predict.mint.spls Predict Method for (mint).(block).(s)pls(da) methods
predict.mint.splsda Predict Method for (mint).(block).(s)pls(da) methods
predict.pls Predict Method for (mint).(block).(s)pls(da) methods
predict.plsda Predict Method for (mint).(block).(s)pls(da) methods
predict.spls Predict Method for (mint).(block).(s)pls(da) methods
predict.splsda Predict Method for (mint).(block).(s)pls(da) methods
print Print Methods for CCA, (s)PLS, PCA and Summary objects
print.pca Print Methods for CCA, (s)PLS, PCA and Summary objects
print.pls Print Methods for CCA, (s)PLS, PCA and Summary objects
print.rcc Print Methods for CCA, (s)PLS, PCA and Summary objects
print.rgcca Print Methods for CCA, (s)PLS, PCA and Summary objects
print.sgcca Print Methods for CCA, (s)PLS, PCA and Summary objects
print.spca Print Methods for CCA, (s)PLS, PCA and Summary objects
print.spls Print Methods for CCA, (s)PLS, PCA and Summary objects
print.summary Print Methods for CCA, (s)PLS, PCA and Summary objects

-- R --

rcc Regularized Canonical Correlation Analysis
rcc.default Regularized Canonical Correlation Analysis

-- S --

select.var Output of selected variables
selectVar Output of selected variables
selectVar.pca Output of selected variables
selectVar.pls Output of selected variables
selectVar.rgcca Output of selected variables
selectVar.sgcca Output of selected variables
selectVar.spls Output of selected variables
sipca Independent Principal Component Analysis
spca Sparse Principal Components Analysis
spls Sparse Partial Least Squares (sPLS)
splsda Sparse Partial Least Squares Discriminant Analysis (sPLS-DA)
srbct Small version of the small round blue cell tumors of childhood data
stemcells Human Stem Cells Data
study_split divides a data matrix in a list of matrices defined by a factor
summary Summary Methods for CCA and PLS objects
summary.pls Summary Methods for CCA and PLS objects
summary.rcc Summary Methods for CCA and PLS objects
summary.spls Summary Methods for CCA and PLS objects

-- T --

tune Generic function to choose the parameters in the different methods in mixOmics
tune.block.splsda Tuning function for block.splsda method (N-integration with sparse Discriminant Analysis)
tune.mint.splsda Estimate the parameters of mint.splsda method
tune.pca Tune the number of principal components in PCA
tune.rcc Estimate the parameters of regularization for Regularized CCA
tune.rcc.default Estimate the parameters of regularization for Regularized CCA
tune.splsda Tuning functions for sPLS-DA method
tune.splslevel Tuning functions for multilevel sPLS method

-- U --

unmap Dummy matrix for an outcome factor

-- V --

vac18 Vaccine study Data
vac18.simulated Simulated data based on the vac18 study for multilevel analysis
vip Variable Importance in the Projection (VIP)

-- W --

withinVariation Within matrix decomposition for repeated measurements (cross-over design)
wrapper.rgcca mixOmics wrapper for Regularised Generalised Canonical Correlation Analysis (rgcca)
wrapper.sgcca mixOmics wrapper for Sparse Generalised Canonical Correlation Analysis (sgcca)
wrapper.sgccda N-integration and feature selection with Projection to Latent Structures models (PLS) with sparse Discriminant Analysis

-- Y --

yeast Yeast metabolomic study