I had same problem once.
You can try this.
just put your number of individuals in the for loops
## estimates the MCP
cp - mcp(rel[,1])
plot(cp)
#make a matrix to receave the data
res - matrix(NA,4,4,dimnames=list(c(
1,2,3,4),
c(1,2,3,4)))
#calc sobreposition
I can't grasp how it can be that the mean prediction at terminal nodes
perfectly fit the true mean values of the observed variable at the terminal
nodes -
I'm afraid I'm missing something completely obviuos here:
# make a regression tree:
rt - ctree(Ozone ~ ., data = airq)
# Validate:
Prediction
Kay,
That is an obvious result of the regression tree algorithm which
recursively splits the data and prediction is given as e.g. mean of
observations at terminal nodes. New data will, however, contribute to
cross validation error, a measure of prediction accuracy. The tree
gives the 'global'
