panel.tskernel {latticeExtra} | R Documentation

## Calculate and plot smoothed time series.

### Description

Plot time series smoothed by discrete symmetric smoothing kernels. These kernels can be used to smooth time series objects. Options include moving averages, triangular filters, or approximately Gaussian filters.

### Usage

    panel.tskernel(x, y, ...,
        width = NROW(x) %/% 10 + 1, n = 300,
        c = 1, sides = 2, circular = FALSE,
        kern = kernel("daniell",
               rep(floor((width/sides) / sqrt(c)), c)))

    simpleSmoothTs(x, ...)

    ## Default S3 method:
    simpleSmoothTs(x, ...,
        width = NROW(x) %/% 10 + 1, n = NROW(x),
        c = 1, sides = 2, circular = FALSE,
        kern = kernel("daniell",
               rep(floor((width/sides)/sqrt(c)), c)))

    ## S3 method for class 'zoo'
    simpleSmoothTs(x, ..., n = NROW(x))

### Arguments

`x, y` | data points. Should define a regular, ordered series. A time series object can be passed as the first argument, in which case `y` can be omitted. The `x` argument given to `simpleSmoothTs` is allowed to be a multivariate time series, i.e. to have multiple columns.
`...` | further arguments passed on to `panel.lines`.
`width` | nominal width of the smoothing kernel in time steps. In the default case, which is a simple moving average, this is the actual width. When `c > 1` the number of time steps used in the kernel increases but the equivalent bandwidth stays the same. If only past values are used (with `sides = 1`) then `width` refers to one side of the symmetric kernel.
`n` | approximate number of time steps desired for the result. If this is less than the length of `x`, the smoothed time series will be aggregated by averaging blocks of (an integer number of) time steps, and this aggregated series will be centered with respect to the original series.
`c` | smoothness of the kernel: `c = 1` is a moving average, `c = 2` is a triangular kernel, `c = 3` and higher approximate smooth Gaussian kernels. `c` is actually the number of times to recursively convolve a simple moving average kernel with itself. The kernel size is adjusted to maintain a constant equivalent bandwidth as `c` increases.
`sides` | if `sides=1` the smoothed series is calculed from past values only (using one half of the symmetric kernel); if `sides=2` it is centred around lag 0.
`circular` | to treat the data as circular (periodic).
`kern` | a `tskernel` object; if given, this over-rides `width` and `c`.

### Note

The author is not an expert on time series theory.

### Author(s)

Felix Andrews felix@nfrac.org

### See Also

`kernel`, `filter`, `xyplot.ts`

### Examples

    ## a Gaussian-like filter (contrast with c = 1 or c = 2)
    xyplot(sunspot.year) +
    layer(panel.tskernel(x, y, width = 20, c = 3, col = 1, lwd = 2))

    ## example from ?kernel:
    ## long and short moving averages, backwards in time
    xyplot(EuStockMarkets[,1]) +
      layer(panel.tskernel(x, y, width = 100, col = 1, sides = 1)) +
      layer(panel.tskernel(x, y, width = 20, col = 2, sides = 1))

    ## per group, with a triangular filter
    xyplot(EuStockMarkets, superpose = TRUE) +
      glayer(panel.tskernel(..., width = 100, c = 2),
             theme = simpleTheme(lwd = 2))

    ## plot the actual kernels used; note adjustment of width
    width = 100
    kdat <- lapply(1:4, function(c) {
        k <- kernel("daniell", rep(floor(0.5*width / sqrt(c)), c))
        ## demonstrate that the effective bandwidth stays the same:
        message("c = ", c, ": effective bandwidth = ", bandwidth.kernel(k))
        ## represent the kernel as a time series, for plotting
        ts(k[-k$m:k$m], start = -k$m)
    })
    names(kdat) <- paste("c =", 1:4)
    xyplot(do.call(ts.union, kdat), type = "h",
        scales = list(y = list(relation = "same")))

[Package latticeExtra version 0.6-28 Index]