Create a scatterplot from a PCA object
Usage
plotPCA(
pca,
pcX = 1,
pcY = 2,
groups = NULL,
individuals = TRUE,
loadings = FALSE,
nLoadings = NULL
)
Arguments
- pca
prcomp
object- pcX
Character: name of the X axis of interest from the PCA
- pcY
Character: name of the Y axis of interest from the PCA
- groups
Matrix: groups to plot indicating the index of interest of the samples (use clinical or sample groups)
- individuals
Boolean: plot PCA individuals
- loadings
Boolean: plot PCA loadings/rotations
- nLoadings
Integer: Number of variables to plot, ordered by those that most contribute to selected principal components (this allows for faster performance as only the most contributing variables are rendered); if
NULL
, all variables are plotted
See also
Other functions to analyse principal components:
calculateLoadingsContribution()
,
performPCA()
,
plotPCAvariance()
Examples
pca <- prcomp(USArrests, scale=TRUE)
plotPCA(pca)
#> Error: unable to find an inherited method for function ‘plotPCA’ for signature ‘object = "prcomp"’
plotPCA(pca, pcX=2, pcY=3)
#> Error: unable to find an inherited method for function ‘plotPCA’ for signature ‘object = "prcomp"’
# Plot both individuals and loadings
plotPCA(pca, pcX=2, pcY=3, loadings=TRUE)
#> Error: unable to find an inherited method for function ‘plotPCA’ for signature ‘object = "prcomp"’
# Only plot loadings
plotPCA(pca, pcX=2, pcY=3, loadings=TRUE, individuals=FALSE)
#> Error: unable to find an inherited method for function ‘plotPCA’ for signature ‘object = "prcomp"’