Dimension Reduction, Regression and Discrimination for Chemometrics

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Documentation for package ‘rchemo’ version 0.1-2

• 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
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
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
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