predict.kerfon | R Documentation |
Computation of the prediction based on a functional kernel model
## S3 method for class 'kerfon'
predict(object, ..., newdata=NULL, label, na.rm=TRUE, positive=FALSE)
object |
A |
newdata |
A |
label |
A vector of character giving the dates to associate to the predicted observations. |
na.rm |
Logical. Does the |
positive |
Logical. Does the result must be forced to positive values. |
... |
Additional arguments. |
This function computes one step forward prediction for a
kerfon
model.
Use the newdata
option to input the past values,
and the label
option value to define the labels for the new
observations. Notices that the output as the same length as
newdata
.
In some special context, the user may need to suppress the
na.rm
observations with the na.rm
option, or force the
prediction to be positive with the positive
option (in this
case the result will be maximum of 0 and the predicted value).
A fdata
object.
J. Damon
kerfon
# Simulation of a FARX process
data1 <- simul.farx(m=10,n=400,base=base.simul.far(20,5),
base.exo=base.simul.far(20,5),
d.a=matrix(c(0.5,0),nrow=1,ncol=2),
alpha.conj=matrix(c(0.2,0),nrow=1,ncol=2),
d.rho=diag(c(0.45,0.90,0.34,0.45)),
alpha=diag(c(0.5,0.23,0.018)),
d.rho.exo=diag(c(0.45,0.90,0.34,0.45)),
cst1=0.0)
# Cross validation
model1 <- kerfon(data=data1, x="X", r=10, na.rm=TRUE)
print(model1)
# Predicting values
pred1 <- predict(model1,newdata=select.fdata(data1,date=1:399))
# Persistence
persist1 <- pred.persist(select.fdata(data1,date=1:399),x="X")
# Real values
real1 <- select.fdata(data1,date=2:400)
errors0 <- persist1[[1]]-real1[[1]]
errors1 <- pred1[[1]]-real1[[1]]
# Norm of observations
summary(real1)
# Persistence
summary(as.fdata(errors0))
# kerfon model
summary(as.fdata(errors1))
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