Multivariate Methods with Unbiased Variable Selection
PLS biplot
Check input
Confusion matrix
Effect matrix for the crisp multilevel tutorial
Make custom parameters for internal modelling
Get RMSEP
Get BER
Get number of misclassifications
Get min, mid or max model from Elastic Net modelling
Get variable importance
Get reference distribution for resampling tests
Perform permutation or resampling tests
Subject identifiers for the rye metabolomics regression tutorial
Subject identifiers for the rye metabolomics regression tutorial, usin...
Merge two MUVR class objects
MUVR2 with EN
MUVR2 with PLS and RF
Identify variables with near zero variance
One hot encoding
Plot permutation analysis
Plot predictions
PCA score plot
Plot for comparison of actual model fitness vs permutation/resampling
Plot predictions for PLS regression
Plot stability
Plot validation metric
Plot variable importance ranking
Calculate permutation p-value Calculate perutation p-value of actual m...
Predict outcomes Predict MV object using a MUVR class object and a X t...
Perform matrix pre-processing
Q2 calculation
Wrapper for speedy access to MUVR2 (autosetup of parallelization)
Wrapper for repeated double cross-validation without variable selectio...
Make custom parameters for rdcvNet internal modelling
Sampling from the distribution of something
Report variables belonging to different classes
Microbiota composition in mosquitos for the classification tutorial
Metabolomics data for the rye metabolomics regression tutorial
Metabolomics data for the rye metabolomics regression tutorial, using ...
Village of capture of mosquitos for the classification tutorial
Rye consumption for the rye metabolomics regression tutorial
Rye consumption for the rye metabolomics regression tutorial, using un...
Predictive multivariate modelling for metabolomics. Types: Classification and regression. Methods: Partial Least Squares, Random Forest ans Elastic Net Data structures: Paired and unpaired Validation: repeated double cross-validation (Westerhuis et al. (2008)<doi:10.1007/s11306-007-0099-6>, Filzmoser et al. (2009)<doi:10.1002/cem.1225>) Variable selection: Performed internally, through tuning in the inner cross-validation loop.