pca {autonomics} | R Documentation |
Perform a dimension reduction. Add sample scores, feature loadings, and dimension variances to object.
pca(object, ndim = 2, minvar = 0, verbose = TRUE, plot = FALSE, ...) pls( object, subgroupvar = "subgroup", ndim = 2, minvar = 0, verbose = FALSE, plot = FALSE, ... ) sma(object, ndim = 2, minvar = 0, verbose = TRUE, plot = FALSE, ...) lda( object, subgroupvar = "subgroup", ndim = 2, minvar = 0, verbose = TRUE, plot = FALSE, ... )
object |
SummarizedExperiment |
ndim |
number |
minvar |
number |
verbose |
TRUE (default) or FALSE |
plot |
TRUE/FALSE |
... |
passed to biplot |
subgroupvar |
subgroup svar |
SummarizedExperiment
Aditya Bhagwat, Laure Cougnaud (LDA)
file <- download_data('atkin18.metabolon.xlsx') object <- read_metabolon(file, plot = FALSE) pca(object, plot=TRUE, color = Group) # Principal Component Analysis pls(object, subgroupvar = 'Group') # Partial Least Squares lda(object, subgroupvar = 'Group') # Linear Discriminant Analysis sma(object) # Spectral Map Analysis pca(object, ndim=3) pca(object, ndim=Inf, minvar=5)