fit_loess | R Documentation |

Fits a local regression smoothing spline through a set of ordered pair
retention times. modeling one set of retention times (rty) as a function
on the other set (rtx). Filtering iterations of high residual points are
performed first. Multiple acceptable values of `span`

can be used, with
one value selected through 10-fold cross validation.

fit_loess( object, useID = FALSE, spans = seq(0.2, 0.3, by = 0.02), outlier = c("MAD", "boxplot"), coef = 2, iterFilter = 2, prop = 0.5, weights = 1, rtx = c("min", "max"), rty = c("min", "max"), message = TRUE, control = loess.control(surface = "direct", iterations = 10) )

`object` |
a |

`useID` |
logical. If set to TRUE, matched ID anchors detected from previous step will never be flagged outliers. |

`spans` |
numeric span values (between 0 & 1) used for loess fits |

`outlier` |
Thresholding method for outlier dection. If "MAD", the
threshold is the mean absolute deviation (MAD) times |

`coef` |
numeric (> 1) multiplier for determining thresholds for outliers
(see |

`iterFilter` |
integer number of outlier filtering iterations to perform |

`prop` |
numeric. A point is excluded if deemed a residual in more than this proportion of fits. Must be between 0 & 1. |

`weights` |
Optional user supplied weights for each ordered pair. Must be of length equal to number of anchors (n) or a divisor of (n + 2) |

`rtx` |
ordered pair of endpoints for rtx; if "max" or "min", gives the maximum or minimum rtx, respectively, as model endpoints for rtx |

`rty` |
ordered pair of endpoints for rty; if "max" or "min", gives the maximum or minimum rtx, respectively, as model endpoints for rty |

`message` |
Option to print message indicating function progress |

`control` |
control parameters for loess fits; see: ?loess.control |

`metabCombiner`

object with `model`

slot updated to
contain a fitted loess model

`selectAnchors`

,`fit_gam`

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, tolmz = 0.003, tolQ = 0.3, windy = 0.02) #version 1 p.comb = fit_loess(p.comb, spans = seq(0.2,0.3,0.02), iterFilter = 1) #version 2 (using weights) anchors = getAnchors(p.comb) weights = c(2, rep(1, nrow(anchors)), 2) #weight = 2 to boundary points p.comb = fit_loess(p.comb, spans = seq(0.2,0.3,0.02), weights = weights) #version 3 (using identities) p.comb = selectAnchors(p.comb, useID = TRUE, tolmz = 0.003) p.comb = fit_loess(p.comb, spans = seq(0.2,0.3,0.02), useID = TRUE) #to preview result of fit_loess plot(p.comb, fit = "loess", xlab = "CHEAR Plasma (30 min)", ylab = "Red-Cross Plasma (20 min)", pch = 19, main = "Example fit_loess Result Fit")

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