Javad, You question is a little too broad to be answered definitively. Also, this is not a code writing service. You should make a meaningful attempt and we are here to help when you get stuck.
1. If you want to know if you can do neural nets, the answer is yes. The three packages most commonly used (that I know of) are 'neuralnet', 'nnet' and 'RSNNS'. You should look in to these package documentation for how to use them. There are also many examples online if you simply google them. 2. You question is unclear, are you wanting to predict all the variables (e.g. phosphorus, Total N, etc.) or do you have some metric for eutrophication? What exactly is the model supposed to predict? 3. If you want to know if a neuralnet is appropriate, that is more of a statistical question. It depends more on the question you want to answer. Given your temporal data, you may want to look in to mixed effects models (e.g nlme, lme4) as another potential approach. Regards, On Tue, Jan 20, 2015 at 11:35 PM, javad bayat via R-help < r-help@r-project.org> wrote: > Dear all; > I am the new user of R. I want to simulation or prediction the > Eutrophication of a lake. I have weekly data(almost for two years) for > Total phosphorus, Total N, pH, Chlorophyll a, Alkalinity, Silica. > Can I predict the Eutrophication by Neural Network in R? > How can I simulation the Eutrophication by these parameter? > please help me to write the codes. > many thanks. > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > 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. > -- Dr. Charles Determan, PhD Integrated Biosciences [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.