Omics Data Integration Project

Documentation for package ‘mixOmics’ version 6.29.2

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

'Omics Data Integration Project

-- A --

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

-- B --

background.predict	Calculate prediction areas
biplot	biplot methods for 'pca' family
biplot.mixo_pls	biplot methods for 'pca' family
biplot.pca	biplot methods for 'pca' family
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
block_tpls	Tensor block PLS
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
circosPlot.block.pls	circosPlot for DIABLO
circosPlot.block.plsda	circosPlot for DIABLO
circosPlot.block.spls	circosPlot for DIABLO
circosPlot.block.splsda	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
colors	Color Palette for mixOmics

-- D --

dctii_m_transforms	Create m-transform functions to apply the Discrete Cosine Transform 2
diverse.16S	16S microbiome data: most diverse bodysites from HMP

-- E --

estim.regul	Estimate the parameters of regularization for Regularized CCA
explained_variance	Calculates the proportion of explained variance of multivariate components

-- F --

facewise_crossproduct	Tensor cross product
facewise_product	Tensor facewise product
facewise_transpose	Facewise transpose an order-3 tensor
ft	Facewise transpose an order-3 tensor

-- 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	Estimate the parameters of regularization for Regularized CCA
image.tune.rcc	Plot the cross-validation score.
imgCor	Image Maps of Correlation Matrices between two Data Sets
impute.nipals	Impute missing values using NIPALS algorithm
ipca	Independent Principal Component Analysis

-- K --

Koren.16S

16S microbiome atherosclerosis study

-- L --

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

-- M --

map	Classification given Probabilities
mat.rank	Matrix Rank
matrix_to_m_transforms	Create m-transform functions as defined by a matrix
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
m_product	Kilmer's tensor-tensor m-product

-- 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	Estimate the parameters of regularization for Regularized CCA
perf	Compute evaluation criteria for PLS, sPLS, PLS-DA, sPLS-DA, MINT and DIABLO
perf.mint.pls	Compute evaluation criteria for PLS, sPLS, PLS-DA, sPLS-DA, MINT and DIABLO
perf.mint.plsda	Compute evaluation criteria for PLS, sPLS, PLS-DA, sPLS-DA, MINT and DIABLO
perf.mint.spls	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.mixo_pls	Compute evaluation criteria for PLS, sPLS, PLS-DA, sPLS-DA, MINT and DIABLO
perf.mixo_plsda	Compute evaluation criteria for PLS, sPLS, PLS-DA, sPLS-DA, MINT and DIABLO
perf.mixo_spls	Compute evaluation criteria for PLS, sPLS, PLS-DA, sPLS-DA, MINT and DIABLO
perf.mixo_splsda	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
plot.pca	Show (s)pca explained variance plots
plot.perf	Plot for model performance for PSLDA analyses
plot.perf.mint.plsda.mthd	Plot for model performance for PSLDA analyses
plot.perf.mint.splsda.mthd	Plot for model performance for PSLDA analyses
plot.perf.pls	Plot for model performance for PLS analyses
plot.perf.pls.mthd	Plot for model performance for PLS analyses
plot.perf.plsda.mthd	Plot for model performance for PSLDA analyses
plot.perf.sgccda.mthd	Plot for model performance for PSLDA analyses
plot.perf.spls.mthd	Plot for model performance for PLS analyses
plot.perf.splsda.mthd	Plot for model performance for PSLDA analyses
plot.rcc	Canonical Correlations Plot
plot.sgccda	Graphical output for the DIABLO framework
plot.tune	Plot model performance
plot.tune.block.splsda	Plot model performance
plot.tune.rcc	Plot the cross-validation score.
plot.tune.spca	Plot model performance
plot.tune.spls	Plot model performance
plot.tune.spls1	Plot model performance
plot.tune.splsda	Plot model performance
plotArrow	Arrow sample plot
plotDiablo	Graphical output for the DIABLO framework
plotIndiv	Plot of Individuals (Experimental Units)
plotIndiv.mint.pls	Plot of Individuals (Experimental Units)
plotIndiv.mint.plsda	Plot of Individuals (Experimental Units)
plotIndiv.mint.spls	Plot of Individuals (Experimental Units)
plotIndiv.mint.splsda	Plot of Individuals (Experimental Units)
plotIndiv.mixo_pls	Plot of Individuals (Experimental Units)
plotIndiv.pca	Plot of Individuals (Experimental Units)
plotIndiv.rgcca	Plot of Individuals (Experimental Units)
plotIndiv.sgcca	Plot of Individuals (Experimental Units)
plotLoadings	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.mixo_pls	Plot of Loading vectors
plotLoadings.mixo_plsda	Plot of Loading vectors
plotLoadings.mixo_spls	Plot of Loading vectors
plotLoadings.mixo_splsda	Plot of Loading vectors
plotLoadings.pca	Plot of Loading vectors
plotLoadings.pls	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
plotMarkers	Plot the values for multivariate markers in block analyses
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	Predict Method for (mint).(block).(s)pls(da) methods
predict.block.pls	Predict Method for (mint).(block).(s)pls(da) methods
predict.block.spls	Predict Method for (mint).(block).(s)pls(da) methods
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.mixo_pls	Predict Method for (mint).(block).(s)pls(da) methods
predict.mixo_spls	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.ipca	Print Methods for CCA, (s)PLS, PCA and Summary objects
print.mint.pls	Print Methods for CCA, (s)PLS, PCA and Summary objects
print.mint.plsda	Print Methods for CCA, (s)PLS, PCA and Summary objects
print.mint.spls	Print Methods for CCA, (s)PLS, PCA and Summary objects
print.mint.splsda	Print Methods for CCA, (s)PLS, PCA and Summary objects
print.mixo_pls	Print Methods for CCA, (s)PLS, PCA and Summary objects
print.mixo_plsda	Print Methods for CCA, (s)PLS, PCA and Summary objects
print.mixo_spls	Print Methods for CCA, (s)PLS, PCA and Summary objects
print.mixo_splsda	Print Methods for CCA, (s)PLS, PCA and Summary objects
print.pca	Print Methods for CCA, (s)PLS, PCA and Summary objects
print.perf.mint.splsda.mthd	Print Methods for CCA, (s)PLS, PCA and Summary objects
print.perf.pls.mthd	Print Methods for CCA, (s)PLS, PCA and Summary objects
print.perf.plsda.mthd	Print Methods for CCA, (s)PLS, PCA and Summary objects
print.perf.sgccda.mthd	Print Methods for CCA, (s)PLS, PCA and Summary objects
print.perf.splsda.mthd	Print Methods for CCA, (s)PLS, PCA and Summary objects
print.predict	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.sgccda	Print Methods for CCA, (s)PLS, PCA and Summary objects
print.sipca	Print Methods for CCA, (s)PLS, PCA and Summary objects
print.spca	Print Methods for CCA, (s)PLS, PCA and Summary objects
print.summary	Print Methods for CCA, (s)PLS, PCA and Summary objects
print.tune.block.splsda	Print Methods for CCA, (s)PLS, PCA and Summary objects
print.tune.mint.splsda	Print Methods for CCA, (s)PLS, PCA and Summary objects
print.tune.pca	Print Methods for CCA, (s)PLS, PCA and Summary objects
print.tune.pls	Print Methods for CCA, (s)PLS, PCA and Summary objects
print.tune.rcc	Print Methods for CCA, (s)PLS, PCA and Summary objects
print.tune.spca	Print Methods for CCA, (s)PLS, PCA and Summary objects
print.tune.spls1	Print Methods for CCA, (s)PLS, PCA and Summary objects
print.tune.splsda	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.mixo_pls	Output of selected variables
selectVar.mixo_spls	Output of selected variables
selectVar.pca	Output of selected variables
selectVar.rgcca	Output of selected variables
selectVar.sgcca	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.mixo_pls	Summary Methods for CCA and PLS objects
summary.mixo_spls	Summary Methods for CCA and PLS objects
summary.pca	Summary Methods for CCA and PLS objects
summary.rcc	Summary Methods for CCA and PLS objects

-- T --

tpca	Tensor PCA-like dimensionality reduction
tpls	Tensor PLS-like regression
tplsda	Run tensor PLSDA-like analysis. Note this always returns a compressed-matrix form output.
tsvdm	Tensor SVD-like decomposition algorithm
tune	Wrapper function to tune pls-derived methods.
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.spca	Tune number of selected variables for spca
tune.spls	Tuning functions for sPLS and PLS functions
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

-- misc --

%fp%	Tensor facewise product