ropls (version 1.4.2)

opls-class: Class "opls"

Description

The opls object class

Objects from the Class

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

Methods

print

signature(object = "opls"): print method for (O)PLS(-DA) models

show

signature(object = "opls"): show method for (O)PLS(-DA) models

plot

signature(object = "opls"): plot method for (O)PLS(-DA) models

predict

signature(object = "opls"): plot method for (O)PLS(-DA) models

coef

signature(object = "opls"):

fitted

signature(object = "opls"): returns predictions of the (O)PLS(-DA) model

residuals

signature(object = "opls"): returns the residuals of the (O)PLS(-DA) regression model

tested

signature(object = "opls"): returns the predictions of the (O)PLS(-DA) trained on the 'subset' observations only

getSummaryDF

signature(object = "opls"): returns summary information about the model

getPcaVarVn

signature(object = "opls"): returns the variance of the components (score vectors)

getScoreMN

signature(object = "opls"): returns the (orthogonal) score matrix of the PCA/(O)PLS(-DA) model

getLoadingMN

signature(object = "opls"): returns the (orthogonal) loading matrix of the PCA/(O)PLS(-DA) model

getWeightMN

signature(object = "opls"): returns the (orthogonal) weight matrix of the (O)PLS(-DA) model

getVipVn

signature(object = "opls"): returns the (orthogonal) VIP values of the (O)PLS(-DA) model

getSubsetVi

signature(object = "opls"): extracts the indices of the samples used for building the model (when a subset argument has been specified)

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)