OPLS-methods {Cardinal} | R Documentation |
Performs orthogonal partial least squares (also called orthogonal projection to latent structures or O-PLS) on an imaging dataset. This will also perform discriminant analysis (O-PLS-DA) if the response is a factor
.
## S4 method for signature 'SImageSet,matrix' OPLS(x, y, ncomp = 20, method = "nipals", center = TRUE, scale = FALSE, keep.Xnew = TRUE, iter.max = 100, ...) ## S4 method for signature 'SImageSet,numeric' OPLS(x, y, ...) ## S4 method for signature 'SImageSet,factor' OPLS(x, y, ...) ## S4 method for signature 'SImageSet,character' OPLS(x, y, ...) ## S4 method for signature 'OPLS' predict(object, newx, newy, keep.Xnew = TRUE, ...)
x |
The imaging dataset on which to perform partial least squares. |
y |
The response variable, which can be a |
ncomp |
The number of O-PLS components to calculate. |
method |
The function used to calculate the projection. |
center |
Should the data be centered first? This is passed to |
scale |
Shoud the data be scaled first? This is passed to |
keep.Xnew |
Should the new data matrix be kept after filtering out the orthogonal variation? |
iter.max |
The number of iterations to perform for the NIPALS algorithm. |
... |
Passed to the next OPLS method. |
object |
The result of a previous call to |
newx |
An imaging dataset for which to calculate their OPLS projection and predict a response from an already-calculated |
newy |
Optionally, a new response from which residuals should be calculated. |
An object of class OPLS
, which is a ResultSet
, where each component of the resultData
slot contains at least the following components:
Xnew
:A new data matrix that has been filtered of the orthogonal variation.
Xortho
:A new data matrix that consists of only the orthogonal variation.
Oscores
:A matrix with the orthogonal component scores for the explanatary variable.
Oloadings
:A matrix objects with the orthogonal explanatory variable loadings.
Oweights
:A matrix with the orthgonal explanatory variable weights.
scores
:A matrix with the component scores for the explanatary variable.
loadings
:A matrix with the explanatory variable loadings.
weights
:A matrix with the explanatory variable weights.
Yscores
:A matrix objects with the component scores for the response variable.
Yweights
:A matrix objects with the response variable weights.
projection
:The projection matrix.
coefficients
:The matrix of the regression coefficients.
ncomp
:The number of O-PLS components.
method
:The method used to calculate the projection.
center
:The center of the dataset. Used for calculating O-PLS scores on new data.
scale
:The scaling factors for the dataset. Used for O-PLS scores on new data.
Ycenter
:The centers of the response variables. Used for predicting new observations.
Yscale
:The scaling factors for the response variables. Used for predicting new observation.
fitted
:The fitted response.
Kylie A. Bemis
Trygg, J., & Wold, S. (2002). Orthogonal projections to latent structures (O-PLS). Journal of Chemometrics, 16(3), 119-128. doi:10.1002/cem.695
PLS
,
PCA
,
spatialShrunkenCentroids
,
sset <- generateImage(diag(4), range=c(200, 300), step=1) y <- factor(diag(4)) opls <- OPLS(sset, y, ncomp=1:2)