| multiblock_plots {multiblock} | R Documentation |
Plot Functions for Multiblock Objects
Description
Plotting procedures for multiblock objects.
Usage
## S3 method for class 'multiblock'
scoreplot(
object,
comps = 1:2,
block = 0,
labels,
identify = FALSE,
type = "p",
xlab,
ylab,
main,
...
)
## S3 method for class 'multiblock'
loadingplot(
object,
comps = 1:2,
block = 0,
scatter = TRUE,
labels,
identify = FALSE,
type,
lty,
lwd = NULL,
pch,
cex = NULL,
col,
legendpos,
xlab,
ylab,
main,
pretty.xlabels = TRUE,
xlim,
...
)
loadingweightplot(object, main = "Loading weights", ...)
## S3 method for class 'multiblock'
biplot(
x,
block = 0,
comps = 1:2,
which = c("x", "y", "scores", "loadings"),
var.axes = FALSE,
xlabs,
ylabs,
main,
...
)
corrplot(object, ...)
## Default S3 method:
corrplot(object, ...)
## S3 method for class 'mvr'
corrplot(object, ...)
## S3 method for class 'multiblock'
corrplot(
object,
comps = 1:2,
labels = TRUE,
col = 1:5,
plotx = TRUE,
ploty = TRUE,
blockScores = FALSE,
...
)
Arguments
object |
|
comps |
|
block |
|
labels |
|
identify |
|
type |
|
xlab |
|
ylab |
|
main |
|
... |
Not implemented. |
scatter |
|
lty |
Vector of line type specifications (see |
lwd |
|
pch |
Vector of point specifications (see |
cex |
|
col |
|
legendpos |
|
pretty.xlabels |
|
xlim |
|
x |
|
which |
|
var.axes |
|
xlabs |
|
ylabs |
|
plotx |
|
ploty |
|
blockScores |
|
Details
Plot functions for scores, loadings and loading.weights based
on the functions found in the pls package.
Value
These plotting routines only generate plots and return no values.
See Also
Overviews of available methods, multiblock, and methods organised by main structure: basic, unsupervised, asca, supervised and complex.
Common functions for computation and extraction of results are found in multiblock_results.
Examples
data(wine)
sc <- sca(wine[c('Smell at rest', 'View', 'Smell after shaking')], ncomp = 4)
loadingplot(sc, block = 1, labels = "names", scatter = TRUE)
scoreplot(sc, labels = "names")
corrplot(sc)
data(potato)
so <- sopls(Sensory ~ NIRraw + Chemical + Compression, data=potato, ncomp = c(2,2,2),
max_comps = 6, validation = "CV", segments = 10)
scoreplot(so, ncomp = c(2,1), block = 3, labels = "names")
corrplot(pcp(so, ncomp = c(2,2,2)))