opls-class: Class "opls"

In ropls: PCA, PLS(-DA) and OPLS(-DA) for multivariate analysis and feature selection of omics data

Description

An S4 class to store PCA and (O)PLS(-DA) models: Objects can be created by calls of the form new("opls", ...) or by calling the opls function

Slots

typeC

character: model type (PCA, PLS, PLS-DA, OPLS, or OPLS-DA)

descriptionMC

character matrix: Description of the data set (number of samples, variables, etc.)

modelDF

data frame with the model overview (number of components, R2X, R2X(cum), R2Y, R2Y(cum), Q2, Q2(cum), significance, iterations)

summaryDF

data frame with the model summary (cumulated R2X, R2Y and Q2); RMSEE is the square root of the mean error between the actual and the predicted responses

subsetVi

Integer vector: Indices of observations in the training data set

pcaVarVn

PCA: Numerical vector of variances of length: predI

vipVn

PLS(-DA): Numerical vector of Variable Importance in Projection; OPLS(-DA): Numerical vector of Variable Importance for Prediction (VIP4,p from Galindo-Prieto et al, 2014)

orthoVipVn

OPLS(-DA): Numerical vector of Variable Importance for Orthogonal Modeling (VIP4,o from Galindo-Prieto et al, 2014)

coefficientMN

(O)PLS(-DA): Numerical matrix of regression coefficients (B; dimensions: ncol(x) x number of responses; B = W*C' and Y = XB + F

xMeanVn

Numerical vector: variable means of the 'x' matrix

xSdVn

Numerical vector: variable standard deviations of the 'x' matrix

yMeanVn

(O)PLS: Numerical vector: variable means of the 'y' response (transformed into a dummy matrix in case it is of 'character' mode initially)

ySdVn

(O)PLS: Numerical vector: variable standard deviations of the 'y' response (transformed into a dummy matrix in case it is of 'character' mode initially)

xZeroVarVi

Numerical vector: indices of variables with variance < 2.22e-16 which were excluded from 'x' before building the model

scoreMN

Numerical matrix of x scores (T; dimensions: nrow(x) x predI) X = TP' + E; Y = TC' + F

loadingMN

Numerical matrix of x loadings (P; dimensions: ncol(x) x predI) X = TP' + E

weightMN

(O)PLS: Numerical matrix of x weights (W; same dimensions as loadingMN)

orthoScoreMN

OPLS: Numerical matrix of orthogonal scores (Tortho; dimensions: nrow(x) x number of orthogonal components)

orthoLoadingMN

OPLS: Numerical matrix of orthogonal loadings (Portho; dimensions: ncol(x) x number of orthogonal components)

orthoWeightMN

OPLS: Numerical matrix of orthogonal weights (same dimensions as orthoLoadingMN)

cMN

(O)PLS: Numerical matrix of Y weights (C); dimensions: number of responses or number of classes in case of qualitative response with more than 2 classes x number of predictive components; Y = TC' + F

coMN

(O)PLS: Numerical matrix of Y orthogonal weights; dimensions: number of responses or number of classes in case of qualitative response with more than 2 classes x number of orthogonal components

uMN

(O)PLS: Numerical matrix of Y scores (U; same dimensions as scoreMN); Y = UC' + G

weightStarMN

Numerical matrix of projections (W*; same dimensions as loadingMN); whereas columns of weightMN are derived from successively deflated matrices, columns of weightStarMN relate to the original 'x' matrix: T = XW*; W*=W(P'W)inv

suppLs

List of additional objects to be used internally by the 'print', 'plot', and 'predict' methods

eset

ExpressionSet: when 'opls' has been applied to an ExpressionSet, the instance with additional columns in pData containing the scores and predictions, and in fData containing the loadings, VIP, coefficients is stored here

Objects from the Class

Objects can be created by calls of the form new("opls", ...) or by calling the opls function

Author(s)

Etienne Thevenot, [e-mail]

Etienne Thevenot, [e-mail]

See Also

opls

Examples

#### PCA

data(foods) ## see Eriksson et al. (2001); presence of 3 missing values (NA)
head(foods)
foodMN <- as.matrix(foods[, colnames(foods) != "Country"])
rownames(foodMN) <- foods[, "Country"]
head(foodMN)
foo.pca <- opls(foodMN)

#### PLS with a single response

data(cornell) ## see Tenenhaus, 1998
head(cornell)
cornell.pls <- opls(as.matrix(cornell[, grep("x", colnames(cornell))]),
                    cornell[, "y"])

## Complementary graphics

plot(cornell.pls, typeVc = c("outlier", "predict-train", "xy-score", "xy-weight"))

#### PLS with multiple (quantitative) responses

data(lowarp) ## see Eriksson et al. (2001); presence of NAs
head(lowarp)
lowarp.pls <- opls(as.matrix(lowarp[, c("glas", "crtp", "mica", "amtp")]),
                   as.matrix(lowarp[, grepl("^wrp", colnames(lowarp)) |
                                      grepl("^st", colnames(lowarp))]))

#### PLS-DA

data(sacurine)
attach(sacurine)
sacurine.plsda <- opls(dataMatrix, sampleMetadata[, "gender"])

#### OPLS-DA

sacurine.oplsda <- opls(dataMatrix, sampleMetadata[, "gender"], predI = 1, orthoI = NA)

detach(sacurine)

ropls documentation built on Nov. 8, 2020, 7:46 p.m.