plsr: PCR and PLSR Models

In mlesnoff/rnirs: Dimension reduction, Regression and Discrimination for Chemometrics

PCR and PLSR Models

Description

Regression on PCA or PLS latent variables (scores).

Functions pcr and plsr assume PCA and PLS algorithms providing orthogonal scores.

Matrix X is centered before the analyses, but X is not column-wise scaled (there is no argument scale available). If a scaling is needed, the user has to scale X before using the functions.

Row observations can eventually be weighted (using argument weights).

Usage

pcr(Xr, Yr, Xu, Yu = NULL, ncomp, algo = NULL, ...)

plsr(Xr, Yr, Xu, Yu = NULL, ncomp, algo = NULL, ...)

Arguments

Xr	A n x p matrix or data frame of reference (= training) observations.
Yr	A n x q matrix or data frame, or a vector of length n, of reference (= training) responses.
Xu	A m x p matrix or data frame of new (= test) observations to predict.
Yu	A m x q matrix or data frame, or a vector of length m, of the true responses for Xu. Default to NULL.
ncomp	The maximal number of PLS scores (= components = latent variables) to consider.
algo	For plsr, a function (algorithm) implementing a PLS on the training data set. Default to NULL (pls_kernel is used). For pcr, a function (algorithm) implementing a PCA. Default to NULL: if n < p, pca_eigenk is used; in the other case, pca_eigen is used.
...	Optionnal arguments to pass in the function defined in algo.

Value

A list of outputs (see examples), such as:

y	Responses for the test data.
fit	Predictions for the test data.
r	Residuals for the test data.

Examples

n <- 10
p <- 6
set.seed(1)
X <- matrix(rnorm(n * p, mean = 10), ncol = p)
y1 <- 100 * rnorm(n)
y2 <- 100 * rnorm(n)
Y <- cbind(y1, y2)
set.seed(NULL)

Xr <- X[1:8, ] ; Yr <- Y[1:8, ] 
Xu <- X[9:10, ] ; Yu <- Y[9:10, ] 

ncomp <- 3
fm <- plsr(Xr, Yr, Xu, Yu, ncomp = ncomp)
#fm <- pcr(Xr, Yr, Xu, Yu, ncomp = ncomp)
names(fm)
fm$y
fm$fit
fm$r
names(fm)

plsr(Xr, Yr, Xu, ncomp = ncomp)[c("y", "fit", "r")]

plsr(Xr, Yr[, 1], Xu, Yu[, 1], ncomp = ncomp)[c("y", "fit", "r")]

####### MSE

ncomp <- 3
fm <- plsr(Xr, Yr, Xu, Yu, ncomp = ncomp)
mse(fm, ~ ncomp, nam = "y1")
mse(fm, ~ ncomp, nam = "y2")
mse(fm, ~ ncomp)

z <- mse(fm, ~ ncomp)
z
z[z$msep == min(z$msep), ]
plotmse(z)

####### B-COEFFICIENTS FOR THE MODEL WITH ncomp COMPONENTS

bcoef(fm, ncomp = 2)

####### Weighted PLSR

# fictive weights
plsr(Xr, Yr, Xu, Yu, ncomp = ncomp, weights = 1:nrow(Xr))

mlesnoff/rnirs documentation built on April 24, 2023, 4:17 a.m.