[R] Uncertainty propagation

[Maayt]

I have a small model running under R. This is basically running various
power-law relations on a variable (in this case water level in a river)
changing spatially and through time. I'd like to include some kind of error
propagation to this.
My first intention was to use a kind of monte carlo routine and run the
model many times by changing the power law parameters. These power laws were
obtained by fitting data points under R. I thus have std error associated to
them: alpha (±da) * WaterHight ^ beta (±db). Is it statistically correct to
sample alpha and beta for each run by picking them from a normal
distribution centered on alpha (resp. beta) with a standard deviation of da
(resp. db) and to perform my statistics (mean and standrad edviation of the
model result) on the model output?
It seems to me that da and db are correlated in some way and by doing what I
entended to, I would overestimate the final error of my model...
My statistical skills are rather weak, is there a way people usually deal
with this kind of problems?

Thanks

-- 
View this message in context: http://r.789695.n4.nabble.com/Uncertainty-propagation-tp2713499p2713499.html
Sent from the R help mailing list archive at Nabble.com.