Plotting functions for ROSA models

Various plotting procedures for rosa objects.

Usage

# 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),
  ...
)

Arguments

x

A rosa object

type

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").

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 image.rosa.

mar

Figure margins, default = c(5,6,4,7) for image.rosa.

las

Axis text direction, default = 1 for image.rosa.

...

Additional parameters passed to loadingplot, image, axis, color.legend, or barplot.

height

A rosa object.

Value

No return.

Details

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.

References

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.

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 in rosa_results.

Examples

data(potato)
mod <- rosa(Sensory[,1] ~ ., data = potato, ncomp = 5)
image(mod)

barplot(mod)

loadingweightplot(mod)