latticeExtra (version 0.6-11)

panel.tskernel: 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, ...)

## S3 method for class 'default':
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

...

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

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 wil

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 recursive

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.

See Also

kernel, filter, xyplot.ts

Examples

.featured_example <- TRUE
## 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")))