Hello all, I'm a relatively new user of R, having mostly used it only for plotting so far. I'm also not very familiar with regression methods, hence forgive my greenness on the topic.
What I want to do in R is multivariate nonparametric regression, with a slight hitch. From my experimental data I have a multitude of samples whose values approximate a function `f' that is defined over a 5D space (i.e., f: R^5->R). The values of the collected samples, call these `y', approximate `f', but due to the process by which they are collected, they always over-estimate (i.e., y = f + e, e >= 0). The distribution of the error `e' can likely be modelled using the positive half of the normal distribution. Naturally I'm trying to obtain a smooth and relatively faithful approximation of `f' using the collected samples `y'. What would be the most fruitful approach in R to doing this? Even suggestions on which package/function to use would be tremendously helpful, as I don't yet know what their strengths/weaknesses are. Also, I would consider parametric regression as well, but in the general case I don't think I can assume/guess for my data at what the appropriate parametric basis functions should be... -- Maciej Kalisiak <[EMAIL PROTECTED]> http://www.dgp.toronto.edu/~mac/ ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
