Validate the internal structure of a DSM object and return a list with information about the object.
check.dsm(model, validate = FALSE, nonneg.check = FALSE)
Aborts with error message if any inconsistency is detected. Otherwise a list with the following items is returned:
number of rows (target terms) of the DSM
number of columns (features) of the DSM
sample size of the underlying data set (may be NA)
whether co-occurrence frequency matrix \(M\) is available
whether \(M\) is sparse or dense (only present if M$ok)
whether \(M\) is in canonical DSM format (only present if M$ok)
whether \(M\) is non-negative (only present if M$ok, and may be NA unless nonneg.check=TRUE was specified)
whether score matrix \(S\) is available
whether \(S\) is sparse or dense (only present if S$ok)
whether \(S\) is in canonical DSM format (only present if S$ok)
whether \(S\) is non-negative (only present if S$ok, and may be NA unless nonneg.check=TRUE was specified)
TRUE if matrix combines data with inconsistent row or column marginals (in this case, association scores cannot be computed any more)
an object of class dsm
carry out extended validation of internal consistency? (may be expensive)
if TRUE, check the co-occurrence (\(M\)) and/or score (\(S\)) matrix for non-negativity (may be expensive)
Stephanie Evert (https://purl.org/stephanie.evert)
dsm, print.dsm