LongituRF (version 0.9)

predict.longituRF: Predict with longitudinal trees and random forests.

Description

Predict with longitudinal trees and random forests.

Usage

# S3 method for longituRF
predict(object, X, Z, id, time, ...)

Arguments

object

: a longituRF output of (S)MERF; (S)REEMforest; (S)MERT or (S)REEMtree function.

X

[matrix]: matrix of the fixed effects for the new observations to be predicted.

Z

[matrix]: matrix of the random effects for the new observations to be predicted.

id

[vector]: vector of the identifiers of the new observations to be predicted.

time

[vector]: vector of the time measurements of the new observations to be predicted.

...

: low levels arguments.

Value

vector of the predicted output for the new observations.

Examples

# NOT RUN {
set.seed(123)
data <- DataLongGenerator(n=20) # Generate the data composed by n=20 individuals.
REEMF <- REEMforest(X=data$X,Y=data$Y,Z=data$Z,id=data$id,time=data$time,mtry=2,ntree=500,sto="BM")
# Then we predict on the learning sample :
pred.REEMF <- predict(REEMF, X=data$X,Z=data$Z,id=data$id, time=data$time)
# Let's have a look at the predictions
# the predictions are in red while the real output trajectories are in blue:
par(mfrow=c(4,5),mar=c(2,2,2,2))
for (i in unique(data$id)){
  w <- which(data$id==i)
  plot(data$time[w],data$Y[w],type="l",col="blue")
  lines(data$time[w],pred.REEMF[w], col="red")
}
# Train error :
mean((pred.REEMF-data$Y)^2)

# The same function can be used with a fitted SMERF model:
smerf <-MERF(X=data$X,Y=data$Y,Z=data$Z,id=data$id,time=data$time,mtry=2,ntree=500,sto="BM")
pred.smerf <- predict(smerf, X=data$X,Z=data$Z,id=data$id, time=data$time)
# Train error :
mean((pred.smerf-data$Y)^2)
# This function can be used even on a MERF model (when no stochastic process is specified)
merf <-MERF(X=data$X,Y=data$Y,Z=data$Z,id=data$id,time=data$time,mtry=2,ntree=500,sto="none")
pred.merf <- predict(merf, X=data$X,Z=data$Z,id=data$id, time=data$time)
# Train error :
mean((pred.merf-data$Y)^2)

# }