Description Usage Arguments Details Value See Also
Using either an ontology_index
object and numeric vector of information content per term  or a matrix of betweenterm similarities (e.g. the output of get_term_sim_mat
), create a numeric matrix of ‘betweenterm set’ similarities. Either the ‘bestmatchaverage’ or ‘bestmatchproduct’ approach (i.e. where the 2 scores obtained by applying the asymmetric ‘bestmatch’ similarity function to two term sets in each order are combined by taking the average or the product respectively). Either Lin's (default) or Resnik's definition of term similarity can be used. If information_content
is not specified, a default value from descendants_IC
is generated.
1 2  get_sim_grid(ontology, information_content, term_sim_method, term_sim_mat,
term_sets, term_sets2 = term_sets, combine = "average")

ontology 

information_content 
Numeric vector of information contents of terms (named by term) 
term_sim_method 
Character string equalling either "lin" or "resnik" to use Lin or Resnik's expression for the similarity of terms. 
term_sim_mat 
Numeric matrix with rows and columns corresponding to (and named by) term IDs, and cells containing the similarity between the row and column term 
term_sets 
List of character vectors of ontological term IDs. 
term_sets2 
Second set of term sets. 
combine 
Character string  either "average" or "product", indicating whether to use the bestmatchproduct' method, or function accepting two arguments  the first, the similarity matrix obtained by averaging across term sets in 
Note that if any term set within term_sets
has 0 terms associated with it, it will get a similarity of 0 to any other set. If you do not want to compare term sets with no annotation, take care to filter out empty sets first, e.g. by 'term_sets=term_sets[sapply(term_sets, length) > 0]'.
Numeric matrix of pairwise term set similarities.
get_term_sim_mat
get_sim_p
get_asym_sim_grid
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