# NOT RUN {
y <- rnorm(100,0,1)
ourCall <- "predict(arima(x=data,order=c(0,1,1)),n.ahead=h)"
# NOTE that the "data" needs to be used in the call, not "y".
# This way we tell the function, where "y" should be used in the call of the function.
# The default call and values
ourValue <- "pred"
ourRO <- ro(y, h=5, origins=5, ourCall, ourValue)
# We can now plot the results of this evaluation:
plot(ourRO)
# You can also use dolar sign
ourValue <- "$pred"
# And you can have constant in-sample size
ro(y, h=5, origins=5, ourCall, ourValue, ci=TRUE)
# You can ask for several values
ourValue <- c("pred","se")
# And you can have constant holdout size
ro(y, h=5, origins=20, ourCall, ourValue, ci=TRUE, co=TRUE)
#### The following code will give exactly the same result as above,
#### but computed in parallel using all but 1 core of CPU:
# }
# NOT RUN {
ro(y, h=5, origins=20, ourCall, ourValue, ci=TRUE, co=TRUE, parallel=TRUE)
# }
# NOT RUN {
#### If you want to use functions from forecast package, please note that you need to
#### set the values that need to be returned explicitly. There are two options for this.
# Example 1:
# }
# NOT RUN {
ourCall <- "forecast(ets(data), h=h, level=95)"
ourValue <- c("mean", "lower", "upper")
ro(y,h=5,origins=5,ourCall,ourValue)
# }
# NOT RUN {
# Example 2:
# }
# NOT RUN {
ourCall <- "forecast(ets(data), h=h, level=c(80,95))"
ourValue <- c("mean", "lower[,1]", "upper[,1]", "lower[,2]", "upper[,2]")
ro(y,h=5,origins=5,ourCall,ourValue)
# }
# NOT RUN {
#### A more complicated example using the for loop and
#### several time series
x <- matrix(rnorm(120*3,0,1), 120, 3)
## Form an array for the forecasts we will produce
## We will have 4 origins with 6-steps ahead forecasts
ourForecasts <- array(NA,c(6,4,3))
## Define models that need to be used for each series
ourModels <- list(c(0,1,1), c(0,0,1), c(0,1,0))
## This call uses specific models for each time series
ourCall <- "predict(arima(data, order=ourModels[[i]]), n.ahead=h)"
ourValue <- "pred"
## Start the loop. The important thing here is to use the same variable 'i' as in ourCall.
for(i in 1:3){
ourData <- x[,i]
ourForecasts[,,i] <- ro(data=ourData,h=6,origins=4,call=ourCall,
value=ourValue,co=TRUE,silent=TRUE)$pred
}
## ourForecasts array now contains rolling origin forecasts from specific
## models.
##### An example with exogenous variables
x <- rnorm(100,0,1)
xreg <- matrix(rnorm(200,0,1),100,2,dimnames=list(NULL,c("x1","x2")))
## 'counti' is used to define in-sample size of xreg,
## 'counto' - the size of the holdout sample of xreg
ourCall <- "predict(arima(x=data, order=c(0,1,1), xreg=xreg[counti,,drop=FALSE]),
n.ahead=h, newxreg=xreg[counto,,drop=FALSE])"
ourValue <- "pred"
ro(x,h=5,origins=5,ourCall,ourValue)
##### Poisson regression with alm
x <- rpois(100,2)
xreg <- cbind(x,matrix(rnorm(200,0,1),100,2,dimnames=list(NULL,c("x1","x2"))))
ourCall <- "predict(alm(x~., data=xreg[counti,,drop=FALSE], distribution='dpois'),
newdata=xreg[counto,,drop=FALSE])"
ourValue <- "mean"
testRO <- ro(xreg[,1],h=5,origins=5,ourCall,ourValue,co=TRUE)
plot(testRO)
## 'countf' is used to take xreg of the size corresponding to the whole
## sample on each iteration
## This is useful when working with functions from smooth package.
## The following call will return the forecasts from es() function of smooth.
# }
# NOT RUN {
ourCall <- "es(data=data, h=h, xreg=xreg[countf,,drop=FALSE])"
\dontrun{ourValue <- "forecast"}
\dontrun{ro(x,h=5,origins=5,ourCall,ourValue)}
# }
# NOT RUN {
# }
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