fit_loess {metabCombiner} | R Documentation

## Fit RT Projection Model With LOESS

### Description

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.

### Usage

    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,
      message = TRUE,
      control = loess.control(surface = "direct", iterations = 10)
    )

### Arguments

`object` | a `metabCombiner` object.
`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`; if "boxplot", the threshold is `coef` times IQR plus 3rd quartile of a model's absolute residual values.
`coef` | numeric (> 1) multiplier for determining thresholds for outliers (see `outlier` argument)
`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)
`message` | Option to print message indicating function progress
`control` | control parameters for loess fits; see: ?loess.control

### Value

`metabCombiner` object with `model` slot updated to contain a fitted loess model

### See Also

`selectAnchors`,`fit_gam`

### 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, 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")

[Package metabCombiner version 1.4.0 Index]