Maayt <m.lupker <at> hotmail.com> writes: [snip] > 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...
How have you fitted the models? Many of the fitting procedures in R give you access not just to the standard errors of the parameters, but also to their correlations/ covariances. If you have this information, you can sample the pairs of parameters from an appropriate multivariate normal distribution. Typically you could do something like ... params <- MASS::mvrnorm(1000,mu=coef(modelfit),Sigma=vcov(modelfit)) predictions <- apply(params,1,predictfun) ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
