kernel {ts}

R Documentation

Smoothing Kernel Objects

Description

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.

Usage

kernel(coef, m, r, name)

df.kernel(k)
bandwidth.kernel(k)
is.tskernel(k)

## S3 method for class 'tskernel'
print(x, digits = max(3,getOption("digits")-3), ...)
## S3 method for class 'tskernel'
plot(x, ...)

Arguments

coef	the upper half of the smoothing kernel coefficients (inclusive of coefficient zero) or the name of a kernel (currently "daniell", "dirichlet", "fejer" or "modified.daniell".
m	the kernel dimension. The number of kernel coefficients is 2*m+1.
name	the name of the kernel.
r	the kernel order for a Fejer kernel.
digits	the number of digits to format real numbers.
k, x	a "tskernel" object.
...	arguments passed to or from other methods.

Details

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).

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 (1991), p. 201, with a continuity correction.

Value

kernel returns a list with class "tskernel", and components the coefficients coef and the kernel dimension m. An additional attribute is "name".

Author(s)

A. Trapletti; modifications by B.D. Ripley

References

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.

See Also

kernapply

Examples

data(EuStockMarkets)    # Demonstrate a simple trading strategy for the 
x <- EuStockMarkets[,1]  # financial time series German stock index DAX.
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

data(sunspot)     # Reproduce example 10.4.3 from Brockwell and Davies (1991)
spectrum(sunspot.year, kernel=kernel("daniell", c(11,7,3)), log="no")