rosa: Response Oriented Sequential Alternation - ROSA

In multiblock: Multiblock Data Fusion in Statistics and Machine Learning

Response Oriented Sequential Alternation - ROSA

Description

Formula based interface to the ROSA algorithm following the style of the pls package.

Usage

rosa(
  formula,
  ncomp,
  Y.add,
  common.comp = 1,
  data,
  subset,
  na.action,
  scale = FALSE,
  weights = NULL,
  validation = c("none", "CV", "LOO"),
  internal.validation = FALSE,
  fixed.block = NULL,
  design.block = NULL,
  canonical = TRUE,
  ...
)

Arguments

formula	Model formula accepting a single response (block) and predictor block names separated by + signs.
ncomp	The maximum number of ROSA components.
Y.add	Optional response(s) available in the data set.
common.comp	Automatically create all combinations of common components up to length common.comp (default = 1).
data	The data set to analyse.
subset	Expression for subsetting the data before modelling.
na.action	How to handle NAs (no action implemented).
scale	Optionally scale predictor variables by their individual standard deviations.
weights	Optional object weights.
validation	Optional cross-validation strategy "CV" or "LOO".
internal.validation	Optional cross-validation for block selection process, "LOO", "CV3", "CV5", "CV10" (CV-number of segments), or vector of integers (default = FALSE).
fixed.block	integer vector with block numbers for each component (0 = not fixed) or list of length <= ncomp (element length 0 = not fixed).
design.block	integer vector containing block numbers of design blocks
canonical	logical indicating if canonical correlation should be use when calculating loading weights (default), enabling B/W maximization, common components, etc. Alternatively (FALSE) a PLS2 strategy, e.g. for spectra response, is used.
...	Additional arguments for cvseg or rosa.fit

Details

ROSA is an opportunistic method sequentially selecting components from whichever block explains the response most effectively. It can be formulated as a PLS model on concatenated input block with block selection per component. This implementation adds several options that are not described in the literature. Most importantly, it opens for internal validation in the block selection process, making this more robust. In addition it handles design blocks explicitly, enables classification and secondary responses (CPLS), and definition of common components.

Value

An object of classes rosa and mvr having several associated printing (rosa_results) and plotting methods (rosa_plots).

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 and plotting are found in rosa_results and rosa_plots, respectively.

Examples

data(potato)
mod <- rosa(Sensory[,1] ~ ., data = potato, ncomp = 10, validation = "CV", segments = 5)
summary(mod)

# For examples of ROSA results and plotting see 
# ?rosa_results and ?rosa_plots.

multiblock documentation built on Nov. 18, 2023, 5:06 p.m.