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
andmet.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
orFALSE
) 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)