loessByCol {edgeR} | R Documentation

## Locally Weighted Mean By Column

### Description

Smooth columns of matrix by non-robust loess curves of degree 0.

### Usage

    loessByCol(y, x=NULL, span=0.5)
    locfitByCol(y, x=NULL, weights=1, span=0.5, degree=0)

### Arguments

`y` | numeric matrix of response variables.
`x` | numeric covariate vector of length `nrow(y)`, defaults to equally spaced.
`span` | width of the smoothing window, in terms of proportion of the data set. Larger values produce smoother curves.
`weights` | relative weights of each observation, one for each covariate value.
`degree` | degree of local polynomial fit

### Details

Fits a loess curve with degree 0 to each column of the response matrix, using the same covariate vector for each column. The smoothed column values are tricube-weighted means of the original values.

`locfitByCol` uses the `locfit.raw` function of the `locfit` package.

### Value

A list containing a numeric matrix with smoothed columns and a vector of leverages for each covariate value.

`locfitByCol` returns a numeric matrix.

### Author(s)

Aaron Lun for `loessByCol`, replacing earlier R code by Davis McCarthy. Gordon Smyth for `locfitByCol`.

### See Also

`loess`

### Examples

    y <- matrix(rnorm(100*3), nrow=100, ncol=3)
    head(y)
    out <- loessByCol(y)
    head(out$fitted.values)

[Package edgeR version 3.20.9 Index]