Is there an optimal / minimum sample size for attempting to construct a 
classification tree using /rpart/?

I have 27 seagrass disturbance sites (boat groundings) that have been 
monitored for a number of years.  The monitoring protocol for each site 
is identical.  From the monitoring data, I am able to determine the 
level of recovery that each site has experienced.  Recovery is our 
categorical dependent variable with values of none, low, medium, high 
which are based upon percent seagrass regrowth into the injury over 
time.  I wish to be able to predict the level of recovery of future 
vessel grounding sites based upon a number of categorical / continuous 
predictor variables used here including (but not limited to) such 
parameters as:  sediment grain size, wave exposure, original size 
(volume) of the injury, injury age, injury location.

When I run /rpart/, the data is split into only two terminal nodes based 
solely upon values of the original volume of each injury.  No other 
predictor variables are considered, even though I have included about 
six of them in the model.  When I remove volume from the model the same 
thing happens but with injury area - two terminal nodes are formed based 
upon area values and no other variables appear.  I was hoping that this 
was a programming issue, me being a newbie and all, but I really think 
I've got the code right.  Now I am beginning to wonder if my N is too 
small for this method?

-- 
Amy V. Uhrin, Research Ecologist

NOAA, National Ocean Service
Center for Coastal Fisheries and Habitat Research
101 Pivers Island Road
Beaufort, NC 28516
(252) 728-8778
(252) 728-8784 (fax)
Amy.Uhrin@noaa.gov

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