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R Package rchemo

https://github.com/mlesnoff/rchemo

PCA

  • pcaeigen Eigen decomposition
  • pcaeigenk Eigen for wide matrices (kernel form)
  • pcasvd SVD decomposition
  • pcanipals Nipals
  • pcanipalsna Nipals allowing missing data
  • kpca Non Linear Kernel PCA (KPCA) (Scholkopf et al. 2002)
Auxiliary (works also for PLS)
  • scordis Score distances (SD) for a score (T) space
  • odis Orthogonal distances (OD) for a score (T) space
  • xfit Matrix fitting from score (T) and loadings (P) matrices

REGRESSION

Linear

  • lmr Multiple linear regression
  • cglsr: CGLSR algorithm for ill-conditionned systems (Björck 1996)

Partial least squares

  • plskernel "Improved Kernel #1" (Dayal & McGregor 1997)
  • plsnipals Nipals
  • plsrannar Kernel version for wide matrices (Rannar et al. 1994)
  • kplsr Non linear kernel PLSR (KPLSR) (Rosipal & Trejo 2001)
  • dkplsr Direct KPLSR (Bennett & Embrechts 2003)

Auxiliary

  • dfplsr_cg, dfplsr_cov, dfplsr_div: Model complexity estiamtion for PLSR
  • aicplsr: Cp and AIC for PLSR models

Ridge

  • rr Ridge Regression (RR)
  • krr Non linear kernel ridge regression (KRR) = Least-square SVM regression (LS-SVMR)
  • dkrr Direct KRR

Support vector machine

  • svmr SVM regression (SVMR)

K-nearest-neighbors

  • knnr KNN regression (KNNR)
  • lwplsr KNN Locally weighted PLSR (KNN-LWPLSR)

Ensemblist methods

Averaging PLSR models with different numbers of LVs

  • plsr_agg PLSR-AGG
  • lwplsr_agg KNN-LWPLSR-AGG

DISCRIMINATION ANALYSIS

Factorial discrimination analysis (FDA)

  • fda Eigen decomposition of the compromise "inter/intra"
  • fdasvd Weighted SVD decomposition of the class centers

On predicted Y-dummy table

  • lmrda DA on LMR prediction (LMR-DA)
  • plsrda DA on PLSR prediction (PLSR-DA = common "PLSDA")
  • kplsrda DA on KPLSR prediction (KPLSR-DA)
  • rrda DA on RR prediction (RR-DA)
  • krrda DA on KRR prediction (KRR-DA)

Probabilistic

  • lda Linear discriminant analysis (LDA)
  • qda Quadratic discriminant analysis (QDA)
  • plslda LDA on PLS latent variables (LVs) (PLS-LDA)
  • plsqda QDA on PLS LVs (PLS-QDA)

Support vector machine

  • svmda SVMDA (= SVMC)

K-nearest-neighbors

  • knnda KNN-DA
  • lwplsrda KNN Locally weighted PLSR-DA (KNN-LWPLSR-DA)
  • lwplslda KNN Locally weighted PLS-LDA/QDA (KNN-LWPLS-LDA/QDA)

Ensemblist methods

Averaging PLSR models with different numbers of LVs

  • plsrda_agg PLSRDA-AGG
  • lwplsrda_agg KNN-LWPLSR-DA-AGG
  • lwplslda_agg KNN-LWPLS-LDA-AGG
  • lwplslda_agg KNN-LWPLS-QDA-AGG

TUNING MODELS

Validation dataset

  • gridscore Any model
  • gridscorelv Models with LVs (faster)
  • gridscorelb Models with ridge parameter (faster)

Cross-validation (CV)

  • gridcv Any model
  • gridcvlv Models with LVs (faster)
  • gridcvlb Models with ridge parameter (faster)

Auxiliary

  • segmkf Building segments for K-fold CV
  • segmts Building segments for test-set CV

Performance scores

  • msep MSEP
  • rmsep RMSEP
  • sep SEP
  • bias Bias
  • r2 R2
  • cor2 Squared correlation
  • rpd, rpdr Ratio of performance to deviation
  • mse Summary for regression
  • err Classification error rate

Heuristic

  • selwold Wold's criterion for models with LVs

SELECTION OF VARIABLES

  • covsel COVSEL algorithm (Roger et al. 2011)

DATA MANAGEMENT

Pre-processing

  • detrend Polynomial detrend transformation
  • snv Standard-normal-deviation transformation
  • mavg Smoothing by moving average
  • savgol Savitsky-Golay filtering (derivation)
  • dderiv Derivation by finite difference
  • xinterp Resampling of spectra by interpolation methods
  • *rmgap Remove vertical gaps in spectra (e.g. for ASD)
  • eposvd Pre-processing data by external parameter orthogonalization (EPO; Roger et al 2003)

Sampling

  • sampks Kennard-Stone sampling
  • sampdp Duplex sampling
  • sampcla Within-class stratified sampling

Checking

  • checkna Find and count NA values in a data set
  • plotxna Plotting missing data in a matrix
  • checkdupl Find duplicated row observations between two data sets
  • rmdupl Remove duplicated row observations between two data sets

Summary

  • aggmean Centers of classes
  • dtagg Summary statistics with data subsets
  • summ Summary of the quantitative variables of a data set

Multi-block

  • mblocks Makes a list of blocks
  • hconcat Horizontal block concatenation
  • blockscal Block autoscaling

Datasets

  • *asdgap ASD spectra with vertical gaps
  • cassav Tropical shrubs
  • forages Tropical forages
  • octane Gazoline "octane" dataset
  • ozone Los Angeles "ozone" pollution (1976) dataset

GRAPHICS

  • plotsp Plotting spectra, loadings, or more generally row observations of a data set
  • plostsp1 Same as plotsp but one-by-one row
  • plotxy 2-d scatter plot
  • plotjit Jittered plot
  • plotscore Plotting error rates of prediction models

AUXILIARY

  • dmnorm Multivariate normal probability density
  • dummy Dummy table
  • euclsq, *euclsq_mu Euclidean distance matrices
  • mahsq, *mahsq_mu Mahalanobis distance matrices
  • getknn KNN selection
  • krbf, kpol, ktanh Gram matrices for different kernels
  • headm Print the first part of a matrix or data frame
  • locw Working function for locally weighted models
  • matB, matW Between and within covariance matrices
  • pinv Moore-Penrose pseudo-inverse
  • sourcedir Source every R functions in a directory
  • wdist Weights for distances
  • Additional working functions in file zfunctions.R

AUTHOR

Matthieu Lesnoff

matthieu.lesnoff@cirad.fr

How to cite

Lesnoff, M. 2021. R package rchemo: Dimension reduction, Regression and Discrimination for Chemometrics. https://github.com/mlesnoff/rchemo. CIRAD, UMR SELMET, Montpellier, France