qc.from.regionwise.df: Perform data quality check based on a dataframe containing...
In neuroconductor/fsbrain: Managing and Visualizing Brain Surface Data

Description Usage Arguments Value See Also

Determine subjects that potentially failed segmentation, based on region-wise data. The data can be anything, but there must be one numerical value per subject per region.

qc.from.regionwise.df(
  rdf,
  z_threshold = 2.8,
  verbosity = 0L,
  num_bad_regions_allowed = 1L
)

`rdf`	data.frame, the region data. The first column must contain the subject identifier, all other columns should contain numerical data for a single region. (Each row represents a subject.) This can be produced by calling `group.agg.atlas.native`.
`z_threshold`	numerical, the cutoff value for considering a subject an outlier (in standard deviations).
`verbosity`	integer, controls the output to stdout. 0=off, 1=normal, 2=verbose.
`num_bad_regions_allowed`	integer, the number of regions in which subjects are allowed to be outliers without being reported as potentially failed segmentation

named list with entries: 'failed_subjects': vector of character strings, the subject identifiers which potentially failed segmentation. 'mean_dists_z': distance to mean, in standard deviations, per subject per region. 'num_outlier_subjects_per_region': number of outlier subjects by region. 'metadata': named list of metadata, e.g., hemi, atlas and measure used to compute these QC results.

Other quality check functions: qc.for.group(), qc.from.segstats.table()

neuroconductor/fsbrain documentation built on Sept. 16, 2020, 2:30 p.m.