PLS-methods {Cardinal} | R Documentation |
Performs partial least squares (also called projection to latent structures or PLS) on an imaging dataset. This will also perform discriminant analysis (PLS-DA) if the response is a factor
. Orthogonal partial least squares options (O-PLS and O-PLS-DA) are also available.
## S4 method for signature 'SparseImagingExperiment,ANY' PLS(x, y, ncomp = 3, method = c("pls", "opls"), center = TRUE, scale = FALSE, iter.max = 100, ...) ## S4 method for signature 'SparseImagingExperiment,ANY' OPLS(x, y, ncomp = 3, ...) ## S4 method for signature 'PLS2' predict(object, newx, newy, ncomp, ...) ## S4 method for signature 'PLS2' fitted(object, ...) ## S4 method for signature 'PLS2' summary(object, ...) ## S4 method for signature 'SImageSet,matrix' PLS(x, y, ncomp = 3, method = "nipals", center = TRUE, scale = FALSE, iter.max = 100, ...) ## S4 method for signature 'SImageSet,ANY' PLS(x, y, ...) ## S4 method for signature 'SImageSet,matrix' OPLS(x, y, ncomp = 3, method = "nipals", center = TRUE, scale = FALSE, keep.Xnew = TRUE, iter.max = 100, ...) ## S4 method for signature 'SImageSet,ANY' OPLS(x, y, ...) ## S4 method for signature 'PLS' predict(object, newx, newy, ...) ## 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 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 |
iter.max |
The number of iterations to perform for the NIPALS algorithm. |
... |
Passed to the next PLS method. |
object |
The result of a previous call to |
newx |
An imaging dataset for which to calculate their PLS projection and predict a response from an already-calculated |
newy |
Optionally, a new response from which residuals should be calcualted. |
keep.Xnew |
Should the new data matrix be kept after filtering out the orthogonal variation? |
An object of class PLS2
, which is a ImagingResult
, or an object of class PLS
, which is a ResultSet
. Each elemnt of resultData
slot contains at least the following components:
fitted
:The fitted response.
loadings
:A matrix with the explanatory variable loadings.
weights
:A matrix with the explanatory variable weights.
scores
:A matrix with the component scores for the explanatary variable.
Yscores
:A matrix objects with the component scores for the response variable.
Yweights
:A matrix objects with the response variable weights.
coefficients
:The matrix of the regression coefficients.
The following components may also be available for classes OPLS
and OPLS2
.
Oloadings
:A matrix objects with the orthogonal explanatory variable loadings.
Oweights
:A matrix with the orthgonal explanatory variable weights.
If y
is a categorical variable, then a categorical class
prediction will also be available in addition to the fitted
numeric 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
PCA
,
spatialShrunkenCentroids
,
setCardinalBPPARAM(SerialParam()) set.seed(1) x <- simulateImage(preset=2, npeaks=10, dim=c(10,10), snoise=1, sdpeaks=1, representation="centroid") y <- makeFactor(circle=pData(x)$circle, square=pData(x)$square) pls <- PLS(x, y, ncomp=1:3) summary(pls) opls <- OPLS(x, y, ncomp=1:3) summary(pls)