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)))