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", ngrid=15, display="plot", ...)
y |
vector of the reponse 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 1:length(y) .
|
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 "no.cor" , "direct" (default), and "indirect" .
|
ngrid |
the number of points to be considered in (minh,maxh) .
|
display |
if this is equal to "plot" , graphical output is produced on the
current graphical device.
|
... |
additional graphical parameters. |
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 $aux
added which contains
the initial value h.first
, the estimated curve using h.first
,
the autocorrelation function of the residuals from the initial fit,
and the residuals.
a new suggested value for h is printed, and, if the parameter display
is equal to "plot"
, graphical output is produced on the current graphical
device.
Bowman, A.W. and Azzalini, A. (1997). Applied Smoothing Techniques for Data Analysis: the Kernel Approach with S-Plus Illustrations. Oxford University Press, Oxford.
sm.regression
, sm.autoregression
type("Suggested value of h: ", h1) sm1 <- sm.regression.eval.1d(x, y, h = h1, hmult = 1, model = "none") if(missing(x)) x.name <- "time" else x.name <- deparse(substitute(x)) if(display == "plot") { plot(x, y, xlab = x.name, ylab = deparse(substitute(y)), ...) lines(sm1$eval.points, sm1$estimate, col = 2) } sm1$aux <- list(h.first = h.first, first.sm = ym, acf = autocorr, raw.residuals = r) invisible(sm1)