Plot PCA confounds

Visualize principal component scores and loadings returned by read_confounds(..., npcs = ...). When multiple runs are present, the default view facets per run for scores (up to max_panels) and aggregates loadings across runs.

Usage

# S3 method for class 'bids_confounds'
plot(x, view = c("auto", "run", "aggregate"), pcs = NULL, top_n = 8, max_panels = 6, ...)

Arguments

x

A bids_confounds object returned by read_confounds().

view

Character. One of "auto", "run", or "aggregate".

pcs

Integer or character vector of PCs to plot (e.g., 1:5 or c("PC1", "PC2")).

top_n

Integer. Keep the top top_n variables per PC based on absolute loading. Set to NULL to keep all variables.

max_panels

Integer. In view = "auto", facet score plots only when the number of runs is at most max_panels.

...

Unused.

Value

A ggplot object, or a list of ggplot objects when patchwork is not available.

Examples

# \donttest{
tryCatch({
  ds_path <- get_example_bids_dataset("ds000001-fmriprep")
  proj <- bids_project(ds_path, fmriprep = TRUE)
  conf <- read_confounds(proj, npcs = 5)
  plot(conf)
  unlink(ds_path, recursive=TRUE)
}, error = function(e) {
  message("Example requires derivatives dataset with confounds: ", e$message)
})
#> Example requires derivatives dataset with confounds: participants.tsv is missing
# }