rosa_plots | R Documentation |
Various plotting procedures for rosa
objects.
## S3 method for class 'rosa'
image(
x,
type = c("correlation", "residual", "order"),
ncomp = x$ncomp,
col = mcolors(128),
legend = TRUE,
mar = c(5, 6, 4, 7),
las = 1,
...
)
## S3 method for class 'rosa'
barplot(
height,
type = c("train", "CV"),
ncomp = height$ncomp,
col = mcolors(ncomp),
...
)
x |
A |
type |
An optional |
ncomp |
Integer to control the number of components to plot (if fewer than the fitted number of components). |
col |
Colours used for the image and bar plot, defaulting to mcolors(128). |
legend |
Logical indicating if a legend should be included (default = TRUE) for |
mar |
Figure margins, default = c(5,6,4,7) for |
las |
Axis text direction, default = 1 for |
... |
Additional parameters passed to |
height |
A |
Usage of the functions are shown using generics in the examples below. image.rosa
makes an image plot of each candidate score's correlation to the winner or the block-wise
response residual. These plots can be used to find alternative block selection for tweaking
the ROSA model. barplot.rosa
makes barplot of block and component explained variances.
loadingweightsplot
is an adaptation of pls::loadingplot
to plot loading weights.
No return.
Liland, K.H., Næs, T., and Indahl, U.G. (2016). ROSA - a fast extension of partial least squares regression for multiblock data analysis. Journal of Chemometrics, 30, 651–662, doi:10.1002/cem.2824.
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 in rosa_results
.
data(potato)
mod <- rosa(Sensory[,1] ~ ., data = potato, ncomp = 5)
image(mod)
barplot(mod)
loadingweightplot(mod)
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