met.plot_PLS2DScore | R Documentation |
met.plot_PLS2DScore
visualizes clusters of samples based on their similarity in partial least squares-discriminant analysis.
met.plot_PLS2DScore(
mSetObj = NA,
imgName = "PLSDA_2DScore",
format = "pdf",
dpi = NULL,
width = NA,
inx1 = 1,
inx2 = 2,
reg = 0.95,
show = 1,
grey.scale = 0,
subtitle = FALSE,
use.sparse = FALSE,
plot = TRUE,
export = TRUE
)
mSetObj |
Input name of the created mSet object,
Data container after partial least squares-discriminant analysis ( |
imgName |
(Character) Enter a name for the image file (if |
format |
(Character, |
dpi |
(Numeric) resolution of the image file (if |
width |
(Numeric) width of the the image file in inches (if |
inx1 |
(Numeric) Indicate the number of the principal component for the x-axis of the loading plot. |
inx2 |
(Numeric) Indicate the number of the principal component for the y-axis of the loading plot. |
reg |
(Numeric) Enter a number between 0 and 1, 0.95 will display the 95 percent confidence regions, and 0 will not. |
show |
Display sample names, |
grey.scale |
Use grey-scale colors, |
subtitle |
(Logical, |
use.sparse |
(Logical) Use a sparse algorithm ( |
plot |
(Logical, |
export |
(Logical, |
The input mSet object with added scatter plot. The plot can be retrieved from within R via print(mSetObj$imgSet$pls.score2d_PCx_PCy.plot)
.
Nicolas T. Wirth mail.nicowirth@gmail.com Technical University of Denmark License: GNU GPL (>= 2)
adapted from PlotPLS2DScore
(https://github.com/xia-lab/MetaboAnalystR).
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