asca_results | R Documentation |
Standard result computation and extraction functions for ASCA (asca
).
## S3 method for class 'asca'
print(x, ...)
## S3 method for class 'asca'
summary(object, ...)
## S3 method for class 'summary.asca'
print(x, digits = 2, ...)
## S3 method for class 'asca'
loadings(object, factor = 1, ...)
## S3 method for class 'asca'
scores(object, factor = 1, ...)
projections(object, ...)
## S3 method for class 'asca'
projections(object, factor = 1, ...)
x |
|
... |
additional arguments to underlying methods. |
object |
|
digits |
|
factor |
|
Usage of the functions are shown using generics in the examples in asca
.
Explained variances are available (block-wise and global) through blockexpl
and print.rosaexpl
.
Object printing and summary are available through:
print.asca
and summary.asca
.
Scores and loadings have their own extensions of scores()
and loadings()
through
scores.asca
and loadings.asca
. Special to ASCA is that scores are on a
factor level basis, while back-projected samples have their own function in projections.asca
.
Returns depend on method used, e.g. projections.asca
returns projected samples,
scores.asca
return scores, while print and summary methods return the object invisibly.
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.
Overviews of available methods, multiblock
, and methods organised by main structure: basic
, unsupervised
, asca
, supervised
and complex
.
Common functions for plotting are found in asca_plots
.
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