objective {metabCombiner} | R Documentation |
This function evaluates the A, B, C weight parameters in terms of score separability of matching versus mismatching compound alignments. Higher objective function value imply a superior weight parameter selection.
objective( cTable, idtable, A, B, C, minScore, mzdiff, rtdiff, qdiff, rtrange, adductdiff, penalty, matches, mismatches )
cTable |
data frame. Abridged |
idtable |
data frame containing all evaluated identities |
A |
Numeric weight for penalizing m/z differences. |
B |
Numeric weight for penalizing differences between fitted & observed retention times |
C |
Numeric weight for differences in Q (abundance quantiles). |
minScore |
numeric. Minimum score to count towards objective value. |
mzdiff |
numeric differences between feature m/z values |
rtdiff |
Differences between model-projected retention time value & observed retention time |
qdiff |
Difference between feature quantile Q values. |
rtrange |
range of dataset Y retention times |
adductdiff |
Numeric divisors of computed score when non-empty adduct labels do not match |
penalty |
positive numeric penalty wherever S(i,j) > S(i,i), i =/= j |
matches |
integer row indices of identity matches |
mismatches |
list of integer identity row mismatches for each identity |
First, the similarity scores between all grouped features are calculated as
described in scorePairs
Then, the objective value for a similarity S is evaluated as:
OBJ(S) = ∑ h(S(i,i)) - h(S(i, j)) - p(S(i,i) > S(i,j))
-S(i,i) represents the similarity between correct identity alignments
-S(i,j), represents the maximum similarity of i to grouped feature j,
i =/= j (the highest-scoring misalignment)
-h(x) = x if x > minScore
, 0 otherwise
-p(COND) = 0 if the condition is true, and a penalty
value otherwise
This is summed over all labeled compound identities (e.g. idx = idy) shared between input datasets.
A numeric value quantifying total separability of compound match similarity scores from mismatch scores, given A,B,C values