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Result functions for L-PLS objects (lpls)

Source: R/lpls_results.R
lpls_results.Rd

Correlation loading plot, prediction and cross-validation for L-PLS models with class lpls.

Usage

# S3 method for class '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 class 'lpls'
predict(
  object,
  X1new = NULL,
  X2new = NULL,
  X3new = NULL,
  exo.direction = c("X2", "X3"),
  ...
)

lplsCV(object, segments1 = NULL, segments2 = NULL, trace = TRUE)

Arguments

x

lpls object

comps

integer vector of components.

doplot

logical indicating if plotting should be performed.

level

integer vector of length 3 for selecting plot symbol. 1=dots. 2=dimnames.

arrow

integer vector of length 3 indicating arrows (1) or not (0).

xlim

numeric x limits.

ylim

numeric y limits.

samplecol

character for sample colours.

pathcol

character for third colour.

varcol

character for variable colours.

varsize

numeric size of symbols for variables.

sampleindex

integer for selecting samples.

pathindex

integer for selecting in third direction.

varindex

integer for selecting variables.

...

Not implemented.

object

lpls object.

X1new

matrix of new X1 samples.

X2new

matrix of new X2 samples.

X3new

matrix of new X3 samples.

exo.direction

character selecting "X2" or "X3" prediction.

segments1

list of sample segments.

segments2

list of variable segments.

trace

logical indicating if verbose mode should be selected.

Value

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).

See also

Overviews of available methods, multiblock, and methods organised by main structure: basic, unsupervised, asca, supervised and complex.

Examples

# 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
#> Segment 4 of 30 completed
#> Segment 5 of 30 completed
#> Segment 6 of 30 completed
#> Segment 7 of 30 completed
#> Segment 8 of 30 completed
#> Segment 9 of 30 completed
#> Segment 10 of 30 completed
#> Segment 11 of 30 completed
#> Segment 12 of 30 completed
#> Segment 13 of 30 completed
#> Segment 14 of 30 completed
#> Segment 15 of 30 completed
#> Segment 16 of 30 completed
#> Segment 17 of 30 completed
#> Segment 18 of 30 completed
#> Segment 19 of 30 completed
#> Segment 20 of 30 completed
#> Segment 21 of 30 completed
#> Segment 22 of 30 completed
#> Segment 23 of 30 completed
#> Segment 24 of 30 completed
#> Segment 25 of 30 completed
#> Segment 26 of 30 completed
#> Segment 27 of 30 completed
#> Segment 28 of 30 completed
#> 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
#> Segment 6 of 20 completed
#> Segment 7 of 20 completed
#> Segment 8 of 20 completed
#> Segment 9 of 20 completed
#> Segment 10 of 20 completed
#> Segment 11 of 20 completed
#> Segment 12 of 20 completed
#> Segment 13 of 20 completed
#> Segment 14 of 20 completed
#> Segment 15 of 20 completed
#> Segment 16 of 20 completed
#> Segment 17 of 20 completed
#> Segment 18 of 20 completed
#> 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
#> Segment 6 of 10 completed
#> Segment 7 of 10 completed
#> Segment 8 of 10 completed
#> Segment 9 of 10 completed
#> Segment 10 of 10 completed