Correlation loading plot, prediction and cross-validation for L-PLS
models with class lpls.
# S3 method for lpls
plot(
x,
comps = c(1, 2),
doplot = c(TRUE, TRUE, TRUE),
level = c(2, 2, 2),
arrow = c(1, 0, 1),
xlim = c(-1, 1),
ylim = c(-1, 1),
samplecol = 4,
pathcol = 2,
varcol = "grey70",
varsize = 1,
sampleindex = 1:dim(x$corloadings$R22)[1],
pathindex = 1:dim(x$corloadings$R3)[1],
varindex = 1:dim(x$corloadings$R21)[1],
...
)
# S3 method for lpls
predict(
object,
X1new = NULL,
X2new = NULL,
X3new = NULL,
exo.direction = c("X2", "X3"),
...
)
lplsCV(object, segments1 = NULL, segments2 = NULL, trace = TRUE)lpls object
integer vector of components.
logical indicating if plotting should be performed.
integer vector of length 3 for selecting plot symbol. 1=dots. 2=dimnames.
integer vector of length 3 indicating arrows (1) or not (0).
numeric x limits.
numeric y limits.
character for sample colours.
character for third colour.
character for variable colours.
numeric size of symbols for variables.
integer for selecting samples.
integer for selecting in third direction.
integer for selecting variables.
Not implemented.
lpls object.
matrix of new X1 samples.
matrix of new X2 samples.
matrix of new X3 samples.
character selecting "X2" or "X3" prediction.
list of sample segments.
list of variable segments.
logical indicating if verbose mode should be selected.
Nothing is return for plotting (plot.lpls), predicted values are returned for predictions (predict.lpls)
and cross-validation metrics are returned for for cross-validation (lplsCV).
Overviews of available methods, multiblock, and methods organised by main structure: basic, unsupervised, asca, supervised and complex.
# Simulate data set
sim <- lplsData(I = 30, N = 20, J = 5, K = 6, ncomp = 2)
X1 <- sim$X1; X2 <- sim$X2; X3 <- sim$X3
# exo-L-PLS:
lp.exo <- lpls(X1,X2,X3, ncomp = 2)
# Predict X1
pred.exo.X2 <- predict(lp.exo, X1new = X1, exo.direction = "X2")
# Predict X3
pred.exo.X2 <- predict(lp.exo, X1new = X1, exo.direction = "X3")
# endo-L-PLS:
lp.endo <- lpls(X1,X2,X3, ncomp = 2, type = "endo")
# Predict X1 from X2 and X3 (in this case fitted values):
pred.endo.X1 <- predict(lp.endo, X2new = X2, X3new = X3)
# LOO cross-validation horizontally
lp.cv1 <- lplsCV(lp.exo, segments1 = as.list(1:dim(X1)[1]))
#> Segment 1 of 30 completed
#> Segment 2 of 30 completed
#> Segment 3 of 30 completed
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#> Segment 29 of 30 completed
#> Segment 30 of 30 completed
# LOO cross-validation vertically
lp.cv2 <- lplsCV(lp.exo, segments2 = as.list(1:dim(X1)[2]))
#> Segment 1 of 20 completed
#> Segment 2 of 20 completed
#> Segment 3 of 20 completed
#> Segment 4 of 20 completed
#> Segment 5 of 20 completed
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#> Segment 19 of 20 completed
#> Segment 20 of 20 completed
# Three-fold CV, horizontal
lp.cv3 <- lplsCV(lp.exo, segments1 = as.list(1:10, 11:20, 21:30))
#> Segment 1 of 10 completed
#> Segment 2 of 10 completed
#> Segment 3 of 10 completed
#> Segment 4 of 10 completed
#> Segment 5 of 10 completed
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