preprocess: Preprocessing of block data

In multiblock: Multiblock Data Fusion in Statistics and Machine Learning

Preprocessing of block data

Description

This is an interface to simplify preprocessing of one, a subset or all blocks in a multiblock object, e.g., a data.frame (see block.data.frame) or list. Several standard preprocessing methods are supplied in addition to letting the user supply it's own function.

Usage

block.preprocess(
  X,
  block = 1:length(X),
  fun = c("autoscale", "center", "scale", "SNV", "EMSC", "Fro", "FroSq", "SingVal"),
  ...
)

Arguments

X	data.frame or list of data.
block	vector of block(s) to preprocess (integers or characters).
fun	character or function selecting which preprocessing to apply (see Details).
...	additional arguments to underlying functions.

Details

The fun parameter controls the type of preprocessing to be performed:

• autoscale: centre and scale each feature/variable.

• center: centre each feature/variable.

• scale: scale each feature/variable.

• SNV: Standard Normal Variate correction, i.e., centre and scale each sample across features/variables.

• EMSC: Extended Multiplicative Signal Correction defaulting to basic EMSC (2nd order polynomials). Further parameters are sent to EMSC::EMSC.

• Fro: Frobenius norm scaling of whole block.

• FroSq: Squared Frobenius norm scaling of whole block (sum of squared values).

• SingVal: Singular value scaling of whole block (first singular value).

• User defined: If a function is supplied, this will be applied to chosen blocks. Preprocessing can be done for all blocks or a subset. It can also be done in a series of operations to combine preprocessing techniques.

Value

The input multiblock object is preprocessed and returned.

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 multiblock_results and multiblock_plots, respectively.

Examples

data(potato)
# Autoscale Chemical block
potato <- block.preprocess(potato, block = "Chemical", "autoscale")
# Apply SNV to NIR blocks
potato <- block.preprocess(potato, block = 3:4, "SNV")
# Centre Sensory block
potato <- block.preprocess(potato, block = "Sensory", "center")
# Scale all blocks to unit Frobenius norm
potato <- block.preprocess(potato, fun = "Fro")

# Effect of SNV
NIR <- (potato$NIRraw + rnorm(26)) * rnorm(26,1,0.2)
NIRc <- block.preprocess(list(NIR), fun = "SNV")[[1]]
old.par <- par(mfrow = c(2,1), mar = c(4,4,1,1))
matplot(t(NIR), type="l", main = "uncorrected", ylab = "")
matplot(t(NIRc), type="l", main = "corrected", ylab = "")
par(old.par)

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