View source: R/evaluateParams.R

evaluateParams | R Documentation |

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

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