On 05/17/2012 06:30 AM, jackl wrote:
Hi..
Ok here is an example on how I wanted the tree to be implemented in R:
- the tree is, as you wrote, saved as a list of different tree levels
- each tree level is also saved as a list of different nodes in that
specific level
- and for the last part, each node is then saved as a list of functions
example:
tree<- list(root, lvl1, lvl2)
root<- list(node00)
lvl1<- list(node10, node11)
lvl2<- list(node20, node21, node22)
node00<- list(f1,f2,f3)
node10<- list(f1,f2,f3)
node11<- list(f1,f2,f3)
..
note: I wrote f1, f2 and f3 in each node because it is the same function,
just with the different parameter, the stock price at that node.
I tried implementing a tree manually and I found out that the
independences between one node and each childnode cause a
heavy computation power.. (the function f3 contains f3 of the
two childnodes and so on..)
example:
node11$f3<- max(node11$f2, node21$f3, node22$f3)
Im facing this problem even with a tree with 'only' 4-5 layers..
best thanks for any answers
Hi,
First, what problem are you trying to solve? A tree structure is not a
problem... it's a tool, to solve a problem.
Second, a tree data structure is simply a simplified graph structure.
There are many packages in R that deal with and store graph structures.
Two that come to mind are igraph
<http://cran.r-project.org/web/packages/igraph/index.html> and sna
<http://cran.r-project.org/web/packages/sna/>
Since those packages can handle graphs, they can handle trees as well.
As an added bonus, the code is already available and well-tested.
If memory usage is a concern, then look into using a sparse matrix
implementation.
Jason
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