smoothVariogram: Smoothes variogram In BiSeq: Processing and analyzing bisulfite sequencing data

Description

Nonparametric smoothing with kernel regression estimators and adaptable bandwidth for variogram smoothing.

Usage

 `1` ```smoothVariogram(variogram, sill, bandwidth) ```

Arguments

 `variogram` A `list` or a `matrix`. Usually the output of `makeVariogram`. `sill` A `numeric`. The sill (upper bound) of the variogram. See Details. `bandwidth` A numeric vector of same length as the variogram (number of rows). Default: `seq(10,1000, length.out=nrow(variogram))`. See Details.

Details

It is necessary to smooth the variogram. Especially for greater `h` the variogram tends to oscillate strongly. This is the reason why the default bandwidth increases with increasing `h`. Nevertheless, the smoothed variogram may further increase or decrease after a horizontal part (sill). This is mostly due to the small number of observations for high distances. To wipe out this bias it is useful to set the smoothed variogram to a fixed value above a certain `h`, usually the mean value of the horizontal part. If a smoothed value v.sm is greater than `sill` for distance h_{range}, this v.sm and all other smoothed values with h > h_{range} are set to `sill`. Internally, the function `lokerns` from package `lokerns` is used for smoothing.

Value

The variogram matrix (or a list with the variogram matrix) with an additional column of the smoothed `v` values.

Author(s)

Katja Hebestreit

`makeVariogram`, `lokerns`

Examples

 ``` 1 2 3 4 5 6 7 8 9 10``` ```data(vario) # Find out the sill (this is more obvious for larger data sets): plot(vario\$variogram\$v) vario.sm <- smoothVariogram(vario, sill = 0.9) plot(vario\$variogram\$v) lines(vario.sm\$variogram[,c("h", "v.sm")], col = "red") ```

BiSeq documentation built on Nov. 8, 2020, 8:05 p.m.