sm.regression.autocor {sm} | R Documentation |
This function estimates nonparametrically the regression function
of y
on x
when the error terms are serially correlated.
sm.regression.autocor(x = 1:n, y, h.first, minh, maxh, method = "direct", ...)
y |
vector of the response values |
h.first |
the smoothing parameter used for the initial smoothing stage. |
x |
vector of the covariate values; if unset, it is assumed to
be |
minh |
the minimum value of the interval where the optimal smoothing parameter is searched for (default is 0.5). |
maxh |
the maximum value of the interval where the optimal smoothing parameter is searched for (default is 10). |
method |
character value which specifies the optimality criterium adopted;
possible values are |
... |
other optional parameters are passed to the |
see Section 7.5 of the reference below.
a list as returned from sm.regression called with the new value of
smoothing parameter, with an additional term $aux
added which contains
the initial value h.first
, the estimated curve using h.first
,
the autocorrelation function of the residuals from the initial fit,
and the residuals.
a new suggested value for h
is printed; also, if the parameter display
is not equal to "none"
, graphical output is produced on the current
graphical device.
Bowman, A.W. and Azzalini, A. (1997). Applied Smoothing Techniques for Data Analysis: the Kernel Approach with S-Plus Illustrations. Oxford University Press, Oxford.
sm.regression
, sm.autoregression