plot_eigenvectors: Plot time-varying vector components
In ha0ye/portalDS: Compute Dynamic Stability for Rodents at Portal

plot_eigenvectors() visualizes the dominant eigenvector(s) from running the S-map model on the community time series

plot_svd_vectors() visualizes the dominant SVD vector(s) from running the S-map model on the community time series

plot_eigenvectors(eigenvectors, num_values = 1, id_var = "censusdate",
  add_IPR = FALSE, palette_option = "plasma", line_size = 1,
  base_size = 16, plot_file = NULL, width = 6, height = NULL)

plot_svd_vectors(svd_vectors, num_values = 1, id_var = "censusdate",
  add_IPR = FALSE, palette_option = "plasma", line_size = 1,
  base_size = 16, plot_file = NULL, width = 6, height = NULL)

`eigenvectors`	a list of matrices for the eigenvectors: the number of elements in the list corresponds to the time points of the s-map model, and each element is a matrix, where the columns are the eigenvectors, in descending order according to the eigenvalues
`num_values`	the number of eigenvectors to plot
`id_var`	when constructing the long-format tibble, what should be the variable name containing the time index
`add_IPR`	whether to also plot the Inverse Participation Ratio, a numerical quantity that measures how evenly the different components contribute to the eigenvector
`palette_option`	the color palette to use (see `viridis::viridis()` for more info)
`line_size`	the line width for the plot
`base_size`	the base font size
`plot_file`	the filepath to where to save the plot; if `NULL` (the default), then the plot is not saved to a file
`width`	width of the saved plot
`height`	height of the saved plot
`svd_vectors`	a list of matrices for the SVD vectors: the number of elements in the list corresponds to the time points of the s-map model, and each element is a matrix, where the columns are the the SVD vectors, in descending order according to the singular values