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A B C D E G I K L M N P R S T U V W Y
| 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.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 |
| 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 |
| 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 |
| diverse.16S | 16S microbiome data: most diverse bodysites from HMP |
| 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 |
| get.BER | Create confusion table and calculate the Balanced Error Rate |
| get.confusion_matrix | Create confusion table and calculate the Balanced Error Rate |
| 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 |
| Koren.16S | 16S microbiome atherosclerosis study |
| linnerud | Linnerud Dataset |
| liver.toxicity | Liver Toxicity Data |
| logratio.transfo | Log-ratio transformation |
| 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 |
| 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 |
| 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.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.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.rcc | Plot the cross-validation score. |
| 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.mixo_pls | Plot of Individuals (Experimental Units) |
| plotIndiv.mixo_spls | Plot of Individuals (Experimental Units) |
| plotIndiv.pca | 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) |
| 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.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.rcc | Plot of Loading vectors |
| plotLoadings.rgcca | Plot of Loading vectors |
| plotLoadings.sgcca | Plot of Loading vectors |
| plotLoadings.sgccda | 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 | 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.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 Methods for CCA, (s)PLS, PCA and Summary objects | |
| print.mixo_pls | Print Methods for CCA, (s)PLS, PCA and Summary objects |
| print.mixo_spls | Print Methods for CCA, (s)PLS, PCA and Summary objects |
| print.pca | 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.summary | Print Methods for CCA, (s)PLS, PCA and Summary objects |
| rcc | Regularized Canonical Correlation Analysis |
| rcc.default | Regularized Canonical Correlation Analysis |
| 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.rcc | Summary Methods for CCA and PLS objects |
| 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.spls | Tuning functions for sPLS method |
| tune.splsda | Tuning functions for sPLS-DA method |
| tune.splslevel | Tuning functions for multilevel sPLS method |
| unmap | Dummy matrix for an outcome factor |
| vac18 | Vaccine study Data |
| vac18.simulated | Simulated data based on the vac18 study for multilevel analysis |
| vip | Variable Importance in the Projection (VIP) |
| 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 |
| yeast | Yeast metabolomic study |