Locally Weighted Mean By Column
Smooth columns of matrix by non-robust loess curves of degree 0.
loessByCol(y, x=NULL, span=0.5) locfitByCol(y, x=NULL, weights=1, span=0.5, degree=0)
y |
numeric matrix of response variables. |
x |
numeric covariate vector of length |
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 |
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.
A list containing a numeric matrix with smoothed columns and a vector of leverages for each covariate value.
locfitByCol returns a numeric matrix.
Aaron Lun for loessByCol, replacing earlier R code by Davis McCarthy. Gordon Smyth for locfitByCol.
y <- matrix(rnorm(100*3), nrow=100, ncol=3) head(y) out <- loessByCol(y) head(out$fitted.values)
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