| 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.
kernel(coef, m, r, name) df.kernel(k) bandwidth.kernel(k) is.tskernel(k)
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. |
k |
a "tskernel" object. |
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.
kernel returns a list with class "tskernel", and
components the coefficients 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.
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")