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PLS-DA: partial least squares discriminant analysis

Source: R/analyze.R
analyze.plsda.Rd

Perform a partial least squares discriminant analysis on the data.

Usage

analyze.plsda(
  dataSet,
  method = "kernelpls",
  ncomp,
  center = TRUE,
  scale = FALSE
)

Arguments

dataSet

The 2d data set of data.

method

A character string (default = "kernelpls") specifying the multivariate regression method to be used:

  • "kernelpls": Kernel algorithm (Dayal and MacGregor 1997) .

  • "widekernelpls": Wide kernel algorithm (Rännar et al. 1994) .

  • "simpls": SIMPLS algorithm (de Jong 1993) .

  • "oscorespls": NIPALS algorithm (classical orthogonal scores algorithm) (Martens and Næs 1989) .

ncomp

An integer specifying the number of components to include in the model. Defaults to min(n-1, p).

center

A boolean (default = TRUE) indicating whether the variables should be shifted to be zero centered.

scale

A boolean (default = FALSE) indicating whether the variables should be scaled to have unit variance before the analysis takes place.

Value

A list containing the following components:

coefficients

An array of regression coefficients for ncomp components. The dimensions are c(nvar, npred, ncomp), where nvar is the number of variables X (proteins) and npred is the number of predicted variables Y (conditions).

scores

A matrix of scores.

vips

A matrix of variable importance in projection (VIP) scores.

loadings

A matrix of loadings.

loading.weights

A matrix of loading weights.

Xvar

A vector with the amount of X-variance explained by each component.

Xtotvar

Total variance in X.

ncomp

The number of components.

method

The method used to fit the model.

center

Indicates whether centering was applied to the model.

scale

The scaling used.

model

The model frame.

References

Dayal BS, MacGregor JF (1997). “Improved PLS Algorithms.” Journal of Chemometrics, 11(1), 73–85. doi:10.1002/(SICI)1099-128X(199701)11:1<73::AID-CEM435>3.0.CO;2-\%23 .

de Jong S (1993). “SIMPLS: An Alternative Approach to Partial Least Squares Regression.” Chemometrics and Intelligent Laboratory Systems, 18(3), 251–263. doi:10.1016/0169-7439(93)85002-X .

Martens H, Næs T (1989). Multivariate Calibration. Chichester, Wiley, New York, USA. ISBN 0471909793.

Rännar S, Lindgren F, Geladi P, Wold S (1994). “A PLS Kernel Algorithm for Data Sets with Many Variables and Fewer Objects. Part 1: Theory and Algorithm.” Journal of Chemometrics, 8(2), 111–125. doi:10.1002/cem.1180080204 .