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Plot PCA 3D

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Plot PCA in 3D

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Plot principal component analysis (PCA) results in 3D. This function takes the output PCA object of the plot_pca function and visualizes the samples in a 3D space defined by the specified principal components.

The samples are coloured by Group and sized by Time.

Usage

plot_pca_3D(pca, pcs = seq(1, 3))

Arguments

pca

A PCA object returned by the plot_pca() function.

pcs

A numeric vector specifying which three principal components to plot (default is 1:3)

Value

An interactive 3D PCA plot showing the distribution of samples in the space defined by the specified principal components. The samples are coloured by Group and sized by Time.

Examples

data("example")
PC = plot_pca(example_obj, morepc = seq(1, 3))

#> -- variables retained:
#> Meikin, Epn2, Hk2, Relb, Igf2bp1, Tk1, Pik3r5, Endog, Amotl1

#> Warning: Using size for a discrete variable is not advised.

#> Scale for colour is already present.
#> Adding another scale for colour, which will replace the existing scale.
#> Scale for colour is already present.
#> Adding another scale for colour, which will replace the existing scale.
#> Scale for colour is already present.
#> Adding another scale for colour, which will replace the existing scale.

plot_pca_3D(PC, pcs = seq(1, 3))

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>>>>>>> Stashed changes