evaluateParams: Evaluate Similarity Score Parameters

View source: R/evaluateParams.R

evaluateParamsR Documentation

Evaluate Similarity Score Parameters

Description

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.

Usage

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("(", "[", "{")
)

Arguments

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

Details

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

Value

A data frame with the following columns:

A

m/z weight values

B

rt weight values

C

Q weight values

totalScore

objective function evaluation of (A,B,C) weights

Note

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

See Also

calcScores, objective

Examples


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: limiting to groups 1-2000
scores <- evaluateParams(p.comb, minScore = 0.5, groups = seq(1,2000))


hhabra/Combiner documentation built on May 26, 2024, 5:08 p.m.