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met.plot_PLS3DScore visualizes clusters of samples based on their similarity in principal component analysis.

Usage

met.plot_PLS3DScore(
  mSetObj = NA,
  imgName = "PLS3DScore",
  format = "json",
  inx1 = 1,
  inx2 = 2,
  inx3 = 3,
  export = F
)

Arguments

mSetObj

Input name of the created mSet object. Data container after partial least squares-discriminant analysis (met.PLSR.Anal and met.PLSDA.CV).

imgName

(Character) Enter a name for the image file (if export = TRUE).

format

format.

inx1

(Numeric) Indicate the number of the principal component for the x-axis of the score plot.

inx2

(Numeric) Indicate the number of the principal component for the y-axis of the score plot.

inx3

(Numeric) Indicate the number of the principal component for the z-axis of the score plot.

export

(Logical, TRUE or FALSE) Shall the plot be exported as file with the chosen format?

Value

The input mSet object with added 3D scatter plot. The plot can be retrieved from within R by executing met.print_PLS3DScore(mSetObj$imgSet$pls.score3d.plot).

Author

Nicolas T. Wirth mail.nicowirth@gmail.com Technical University of Denmark License: GNU GPL (>= 2)