asca_plots {HDANOVA} | R Documentation |
ASCA Plot Methods
Description
Various plotting procedures for asca
objects.
Usage
## S3 method for class 'asca'
loadingplot(object, factor = 1, comps = 1:2, ...)
## S3 method for class 'asca'
scoreplot(
object,
factor = 1,
comps = 1:2,
within_level = "all",
pch.scores = 19,
pch.projections = 1,
gr.col = NULL,
projections = TRUE,
spider = FALSE,
ellipsoids,
confidence,
xlim,
ylim,
xlab,
ylab,
legendpos,
...
)
permutationplot(object, factor = 1, xlim, xlab = "SSQ", main, ...)
Arguments
object |
|
factor |
|
comps |
|
... |
additional arguments to underlying methods. |
within_level |
MSCA parameter for chosing plot level (default = "all"). |
pch.scores |
|
pch.projections |
|
gr.col |
|
projections |
Include backprojections in score plot (default = TRUE). |
spider |
Draw lines between group centers and backprojections (default = FALSE). |
ellipsoids |
|
confidence |
|
xlim |
|
ylim |
|
xlab |
|
ylab |
|
legendpos |
|
main |
Plot title. |
Details
Usage of the functions are shown using generics in the examples in asca
.
Plot routines are available as
scoreplot.asca
and loadingplot.asca
.
Value
The plotting routines have no return.
References
Smilde, A., Jansen, J., Hoefsloot, H., Lamers,R., Van Der Greef, J., and Timmerman, M.(2005). ANOVA-Simultaneous Component Analysis (ASCA): A new tool for analyzing designed metabolomics data. Bioinformatics, 21(13), 3043–3048.
Liland, K.H., Smilde, A., Marini, F., and Næs,T. (2018). Confidence ellipsoids for ASCA models based on multivariate regression theory. Journal of Chemometrics, 32(e2990), 1–13.
Martin, M. and Govaerts, B. (2020). LiMM-PCA: Combining ASCA+ and linear mixed models to analyse high-dimensional designed data. Journal of Chemometrics, 34(6), e3232.
See Also
Main methods: asca
, apca
, limmpca
, msca
, pcanova
, prc
and permanova
.
Workhorse function underpinning most methods: hdanova
.
Extraction of results and plotting: asca_results
, asca_plots
, pcanova_results
and pcanova_plots