stepar(y, xreg = NULL, trend = c("linear", "quadratic", "constant"),
order = NULL, lead = 0, newx = NULL, output = TRUE, ...)
NULL
.linear
.NULL
.0
.xreg
for predictions. The default is
NULL
.NULL
.ar
function.stepar
" containing the following components:lead
> 0.constant
,linear
,quadratic
)
model with respective to time sequence:
$t = (1:n)/n$, where $n = length(y)$. If xreg
is supplied,
the fitted model is updated by
$$y = \mu + \beta*xreg + e[t]$$
for trend = "constant"
, and
$$y = \mu + \beta*xreg + \alpha*t + e[t]$$
for trend = "linear"
, and
$$y = \mu + \beta*xreg + \alpha[1]*t + \alpha[2]*t^2 + e[t]$$
for trend = "quadratic"
.
The second stage is to fit an autoregressive process to the residuals of the fitted
model obtained in the first stage, which is accomplished by using ar
function
in stats
package.x <- 5*(1:100)/100 x <- x + arima.sim(list(order = c(1,0,0),ar = 0.4),n = 100) stepar(x) stepar(x,order = 1) # with xreg supplied X <- matrix(rnorm(200),100,2) y <- 0.1*X[,1] + 1.2*X[,2] + rnorm(100) stepar(y,X) # make a prediction with lead = 1; used with caution. newdat1 <- matrix(rnorm(2),nrow = 1) fit1 <- stepar(y,X,lead = 1,newx = newdat1,output = FALSE) # make a prediction with lead = 2; used with caution. newdat2 <- matrix(rnorm(4),nrow = 2) fit2 <- stepar(y,X,lead = 2,newx = newdat2,output = FALSE)