Various plotting procedures for rosa objects.
# S3 method for rosa
image(
x,
type = c("correlation", "residual", "order"),
ncomp = x$ncomp,
col = mcolors(128),
legend = TRUE,
mar = c(5, 6, 4, 7),
las = 1,
...
)# S3 method for rosa
barplot(
height,
type = c("train", "CV"),
ncomp = height$ncomp,
col = mcolors(ncomp),
...
)
No return.
A rosa object
An optional character for selecting the plot type. For image.rosa the options are: "correlation" (default), "residual" or "order". For barplot.rosa the options indicate: explained variance should be based on training data ("train") or cross-validation ("CV").
Integer to control the number of components to plot (if fewer than the fitted number of components).
Colours used for the image and bar plot, defaulting to mcolors(128).
Logical indicating if a legend should be included (default = TRUE) for image.rosa.
Figure margins, default = c(5,6,4,7) for image.rosa.
Axis text direction, default = 1 for image.rosa.
Additional parameters passed to loadingplot, image, axis, color.legend, or barplot.
A rosa object.
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.
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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