# Dimension Reduction, Regression and Discrimination for Chemometrics

## Documentation for package ‘rchemo’ version 0.1-3

- DESCRIPTION file.
- Package NEWS.

## Help Pages

A B C D E F G H I K L M O P Q R S T V W X

## -- A --

aggmean | Centers of classes
aicplsr | AIC and Cp for Univariate PLSR Models
asdgap | asdgap

## -- B --

bias | Residuals and prediction error rates
blockscal | Block autoscaling

## -- C --

cassav | cassav
cglsr | CG Least Squares Models
checkdupl | Duplicated rows in datasets
checkna | Find and count NA values in a dataset
coef.Cglsr | CG Least Squares Models
coef.Dkpls | Direct KPLSR Models
coef.Dkrr | Direct KRR Models
coef.Kplsr | KPLSR Models
coef.Krr | KRR (LS-SVMR)
coef.Lmr | Linear regression models
coef.Mbplsr | multi-block PLSR algorithms
coef.Plsr | PLSR algorithms
coef.Rr | Linear Ridge Regression
cor2 | Residuals and prediction error rates
covsel | CovSel

## -- D --

dderiv | Derivation by finite difference
detrend | Polynomial de-trend transformation
dfplsr_cg | Degrees of freedom of Univariate PLSR Models
dfplsr_cov | Degrees of freedom of Univariate PLSR Models
dfplsr_div | Degrees of freedom of Univariate PLSR Models
dkplsr | Direct KPLSR Models
dkrr | Direct KRR Models
dmnorm | Multivariate normal probability density
dtagg | Summary statistics of data subsets
dummy | Table of dummy variables

## -- E --

eposvd | External parameter orthogonalization (EPO)
err | Residuals and prediction error rates
euclsq | Matrix of distances
euclsq_mu | Matrix of distances

## -- F --

fda | Factorial discriminant analysis
fdasvd | Factorial discriminant analysis
forages | forages

## -- G --

getknn | KNN selection
gridcv | Cross-validation
gridcvlb | Cross-validation
gridcvlv | Cross-validation
gridscore | Tuning of predictive models on a validation dataset
gridscorelb | Tuning of predictive models on a validation dataset
gridscorelv | Tuning of predictive models on a validation dataset

## -- H --

hconcat | Block autoscaling
headm | Display of the first part of a data set

## -- I --

interpl | Resampling of spectra by interpolation methods

## -- K --

knnda | KNN-DA
knnr | KNN-R
kpca | KPCA
kplsr | KPLSR Models
kplsrda | KPLSR-DA models
kpol | Kernel functions
krbf | Kernel functions
krr | KRR (LS-SVMR)
krrda | KRR-DA models
ktanh | Kernel functions

## -- L --

lda | LDA and QDA
lmr | Linear regression models
lmrda | LMR-DA models
locw | Locally weighted models
locwlv | Locally weighted models
lodis | Orthogonal distances from a PCA or PLS score space
lwplslda | KNN-LWPLS-DA Models
lwplslda_agg | Aggregation of KNN-LWPLSDA models with different numbers of LVs
lwplsqda | KNN-LWPLS-DA Models
lwplsqda_agg | Aggregation of KNN-LWPLSDA models with different numbers of LVs
lwplsr | KNN-LWPLSR
lwplsrda | KNN-LWPLS-DA Models
lwplsrda_agg | Aggregation of KNN-LWPLSDA models with different numbers of LVs
lwplsr_agg | Aggregation of KNN-LWPLSR models with different numbers of LVs

## -- M --

mahsq | Matrix of distances
mahsq_mu | Matrix of distances
matB | Between and within covariance matrices
matW | Between and within covariance matrices
mavg | Smoothing by moving average
mblocks | Block autoscaling
mbplslda | multi-block PLSDA models
mbplsqda | multi-block PLSDA models
mbplsr | multi-block PLSR algorithms
mbplsrda | multi-block PLSDA models
mbplsr_mbplsda_allsteps | MBPLSR or MBPLSDA analysis steps
mpars | Tuning of predictive models on a validation dataset
mse | Residuals and prediction error rates
msep | Residuals and prediction error rates

## -- O --

octane | octane
odis | Orthogonal distances from a PCA or PLS score space
orthog | Orthogonalization of a matrix to another matrix
ozone | ozone

## -- P --

pcaeigen | PCA algorithms
pcaeigenk | PCA algorithms
pcanipals | PCA algorithms
pcanipalsna | PCA algorithms
pcasph | PCA algorithms
pcasvd | PCA algorithms
pinv | Moore-Penrose pseudo-inverse of a matrix
plotjit | Jittered plot
plotscore | Plotting errors rates
plotsp | Plotting spectra
plotsp1 | Plotting spectra
plotxna | Plotting Missing Data in a Matrix
plotxy | 2-d scatter plot
plskern | PLSR algorithms
plslda | PLSDA models
plslda_agg | PLSDA with aggregation of latent variables
plsnipals | PLSR algorithms
plsqda | PLSDA models
plsqda_agg | PLSDA with aggregation of latent variables
plsrannar | PLSR algorithms
plsrda | PLSDA models
plsrda_agg | PLSDA with aggregation of latent variables
plsr_agg | PLSR with aggregation of latent variables
plsr_plsda_allsteps | PLSR or PLSDA analysis steps
predict.Cglsr | CG Least Squares Models
predict.Dkplsr | Direct KPLSR Models
predict.Dkrr | Direct KRR Models
predict.Dmnorm | Multivariate normal probability density
predict.Knnda | KNN-DA
predict.Knnr | KNN-R
predict.Kplsr | KPLSR Models
predict.Kplsrda | KPLSR-DA models
predict.Krr | KRR (LS-SVMR)
predict.Krrda | KRR-DA models
predict.Lda | LDA and QDA
predict.Lmr | Linear regression models
predict.Lmrda | LMR-DA models
predict.Lwplsprobda | KNN-LWPLS-DA Models
predict.Lwplsprobda_agg | Aggregation of KNN-LWPLSDA models with different numbers of LVs
predict.Lwplsr | KNN-LWPLSR
predict.Lwplsrda | KNN-LWPLS-DA Models
predict.Lwplsrda_agg | Aggregation of KNN-LWPLSDA models with different numbers of LVs
predict.Lwplsr_agg | Aggregation of KNN-LWPLSR models with different numbers of LVs
predict.Mbplsprobda | multi-block PLSDA models
predict.Mbplsr | multi-block PLSR algorithms
predict.Mbplsrda | multi-block PLSDA models
predict.Plsda_agg | PLSDA with aggregation of latent variables
predict.Plsprobda | PLSDA models
predict.Plsr | PLSR algorithms
predict.Plsrda | PLSDA models
predict.Plsr_agg | PLSR with aggregation of latent variables
predict.Qda | LDA and QDA
predict.Rr | Linear Ridge Regression
predict.Rrda | RR-DA models
predict.Soplsprobda | Block dimension reduction by SO-PLS-DA
predict.Soplsr | Block dimension reduction by SO-PLS
predict.Soplsrda | Block dimension reduction by SO-PLS-DA
predict.Svm | SVM Regression and Discrimination

## -- Q --

qda | LDA and QDA

## -- R --

r2 | Residuals and prediction error rates
residcla | Residuals and prediction error rates
residreg | Residuals and prediction error rates
rmgap | Removing vertical gaps in spectra
rmsep | Residuals and prediction error rates
rpd | Residuals and prediction error rates
rpdr | Residuals and prediction error rates
rr | Linear Ridge Regression
rrda | RR-DA models

## -- S --

sampcla | Within-class sampling
sampdp | Duplex sampling
sampks | Kennard-Stone sampling
savgol | Savitzky-Golay smoothing
scordis | Score distances (SD) in a PCA or PLS score space
segmkf | Segments for cross-validation
segmts | Segments for cross-validation
selwold | Heuristic selection of the dimension of a latent variable model with the Wold's criterion
sep | Residuals and prediction error rates
snv | Standard normal variate transformation (SNV)
soplslda | Block dimension reduction by SO-PLS-DA
soplsldacv | Block dimension reduction by SO-PLS-DA
soplsqda | Block dimension reduction by SO-PLS-DA
soplsqdacv | Block dimension reduction by SO-PLS-DA
soplsr | Block dimension reduction by SO-PLS
soplsrcv | Block dimension reduction by SO-PLS
soplsrda | Block dimension reduction by SO-PLS-DA
soplsrdacv | Block dimension reduction by SO-PLS-DA
soplsr_soplsda_allsteps | SOPLSR or SOPLSDA analysis steps
sourcedir | Source R functions in a directory
summ | Description of the quantitative variables of a data set
summary.Fda | Factorial discriminant analysis
summary.Kpca | KPCA
summary.Mbplsr | multi-block PLSR algorithms
summary.Pca | PCA algorithms
summary.Plsr | PLSR algorithms
summary.Svm | SVM Regression and Discrimination
svmda | SVM Regression and Discrimination
svmr | SVM Regression and Discrimination

## -- T --

transform | Generic transform function
transform.Dkpls | Direct KPLSR Models
transform.Fda | Factorial discriminant analysis
transform.Kpca | KPCA
transform.Kplsr | KPLSR Models
transform.Mbplsr | multi-block PLSR algorithms
transform.Pca | PCA algorithms
transform.Plsr | PLSR algorithms
transform.Soplsprobda | Block dimension reduction by SO-PLS-DA
transform.Soplsr | Block dimension reduction by SO-PLS
transform.Soplsrda | Block dimension reduction by SO-PLS-DA

## -- V --

vip | Variable Importance in Projection (VIP)

## -- W --

wdist | Distance-based weights

## -- X --

xfit | Matrix fitting from a PCA or PLS model
xfit.Pca | Matrix fitting from a PCA or PLS model
xfit.Plsr | Matrix fitting from a PCA or PLS model
xresid | Matrix fitting from a PCA or PLS model