smoothVariogram {BiSeq} | R Documentation

## Smoothes variogram

### Description

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

### Usage

    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

### See Also

`makeVariogram`, `lokerns`

### Examples

    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")

[Package BiSeq version 1.30.0 Index]