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
)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.
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 or by parsing a text file produced by the FreeSurfer tool 'aparcstats2table' (see fsbrain:::qc.from.segstats.table for parsing code).
numerical, the cutoff value for considering a subject an outlier (in standard deviations).
integer, controls the output to stdout. 0=off, 1=normal, 2=verbose.
integer, the number of regions in which subjects are allowed to be outliers without being reported as potentially failed segmentation
Other quality check functions:
qc.for.group(),
qc.from.segstats.table()