evaluateParams: Evaluate Similarity Score Parameters
In metabCombiner: Method for Combining LC-MS Metabolomics Feature Measurements

Description Usage Arguments Details Value Note See Also Examples

This function provides a method for guiding selection of suitable values for A, B, & C weight arguments in the calcScores method, based on the similarity scores of shared identified compounds. Datasets must have at least one identity in common (i.e. idx = idy, case-insensitive), and preferably more than 10.

evaluateParams(
  object,
  A = seq(60, 150, by = 10),
  B = seq(6, 15),
  C = seq(0.1, 0.5, by = 0.1),
  fit = c("gam", "loess"),
  usePPM = FALSE,
  minScore = 0.5,
  penalty = 5,
  groups = NULL,
  brackets_ignore = c("(", "[", "{")
)

`object`	metabCombiner object
`A`	Numeric weights for penalizing m/z differences.
`B`	Numeric weights for penalizing differences between fitted & observed retention times
`C`	Numeric weight for differences in Q (abundance quantiles).
`fit`	Character. Choice of fitted rt model, "gam" or "loess."
`usePPM`	logical. Option to use relative parts per million (ppm) as opposed to absolute) m/z differences in score computations.
`minScore`	numeric minimum score to count towards objective function calculation for known matching features (idx = idy) and mismatches.
`penalty`	numeric. Subtractive mismatch penalty.
`groups`	integer. Vector of feature groups to score. If set to NULL (default), will compute scores for all feature groups.
`brackets_ignore`	bracketed identity and adduct character strings of these types will be ignored according to this argument

This uses an objective function, based on the accurate and inaccurate alignments of shared pre-identified compounds. For more details, see: objective.

A data frame with the following columns:

`A`	m/z weight values
`B`	rt weight values
`C`	Q weight values
`score`	objective function evaluation of (A,B,C) weights

In contrast to calcScores function, A, B, & C take numeric vectors as input, as opposed to constants. The total number of rows in the output will be equal to the products of the lengths of these input vectors

calcScores, objective

data(plasma30)
data(plasma20)

p30 <- metabData(plasma30, samples = "CHEAR")
p20 <- metabData(plasma20, samples = "Red", rtmax = 17.25)
p.comb = metabCombiner(xdata = p30, ydata = p20, binGap = 0.0075)

p.comb = selectAnchors(p.comb, windx = 0.03, windy = 0.02)
p.comb = fit_gam(p.comb, k = 20, iterFilter = 2)

#example 1
scores = evaluateParams(p.comb, A = seq(60,100,10), B = seq(10,15), C = 0.5,
    minScore = 0.7, penalty = 10)

##example 2: using PPM mass deviation (note change to A argument)
scores = evaluateParams(p.comb, usePPM = TRUE, A = seq(0.01,0.05,0.01))

##example 3: limiting to groups 1-2000
scores = evaluateParams(p.comb, minScore = 0.5, groups = 1:2000)