| 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")