kernel {stats} | R Documentation |

The `"tskernel"`

class is designed to represent discrete
symmetric normalized smoothing kernels. These kernels can be used to
smooth vectors, matrices, or time series objects.

There are `print`

, `plot`

and `[`

methods for these kernel objects.

kernel(coef, m, r, name) df.kernel(k) bandwidth.kernel(k) is.tskernel(k) ## S3 method for class 'tskernel': plot(x, type = "h", xlab = "k", ylab = "W[k]", main = attr(x,"name"), ...)

`coef` |
the upper half of the smoothing kernel coefficients
(including coefficient zero) or the name of a kernel
(currently `"daniell"` , `"dirichlet"` , `"fejer"` or
`"modified.daniell"` . |

`m` |
the kernel dimension(s). When `m`
has length larger than one, it means the convolution of
kernels of dimension `m[j]` , for `j in 1:length(m)` .
Currently this is supported only for the named "*daniell" kernels. |

`name` |
the name the kernel will be called. |

`r` |
the kernel order for a Fejer kernel. |

`k,x` |
a `"tskernel"` object. |

`type, xlab, ylab, main, ...` |
arguments passed to
`plot.default` . |

`kernel`

is used to construct a general kernel or named specific
kernels. The modified Daniell kernel halves the end coefficients (as
used by S-PLUS).

The `[`

method allows natural indexing of kernel objects
with indices in `(-m) : m`

. The normalization is such that for
`k <- kernel(*)`

, `sum(k[ -k$m : k$m ])`

is one.

`df.kernel`

returns the “equivalent degrees of freedom” of
a smoothing kernel as defined in Brockwell and Davies (1991), page
362, and `bandwidth.kernel`

returns the equivalent bandwidth as
defined in Bloomfield (1976), p. 201, with a continuity correction.

`kernel()`

returns an object of class `"tskernel"`

which is
basically a list with the two components `coef`

and the kernel
dimension `m`

. An additional attribute is `"name"`

.

A. Trapletti; modifications by B.D. Ripley

Bloomfield, P. (1976)
*Fourier Analysis of Time Series: An Introduction.*
Wiley.

Brockwell, P.J. and Davis, R.A. (1991)
*Time Series: Theory and Methods.*
Second edition. Springer, pp. 350–365.

## Demonstrate a simple trading strategy for the ## financial time series German stock index DAX. x <- EuStockMarkets[,1] k1 <- kernel("daniell", 50) # a long moving average k2 <- kernel("daniell", 10) # and a short one plot(k1) plot(k2) x1 <- kernapply(x, k1) x2 <- kernapply(x, k2) plot(x) lines(x1, col = "red") # go long if the short crosses the long upwards lines(x2, col = "green") # and go short otherwise ## More interesting kernels kd <- kernel("daniell", c(3,3)) kd # note the unusual indexing kd[-2:2] plot(kernel("fejer", 100, r=6)) plot(kernel("modified.daniell", c(7,5,3))) # Reproduce example 10.4.3 from Brockwell and Davies (1991) spectrum(sunspot.year, kernel=kernel("daniell", c(11,7,3)), log="no")

[Package *stats* version 2.2.1 Index]