1. As this is not an R question, this is probably not an appropriate list for posting. You might wish to consider a list specifically devoted to statistics and data analysis.
2. Having said that, some kind souls on this list may be willing to help. 3. I believe you have the wrong data structure and need to rethink what the appropriate one is. Local statistical resources, if you have any, may be able to help (as a more intimate understanding of the nature of your study design and goals may be required). 4. I believe your proposal to view your continuous x variable as an indicator for interactions is utter nonsense. Again, if possible consult a local statistician that you can spend some time with. Bert Gunter Genentech Nonclinical Statistics -----Original Message----- From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On Behalf Of William Simpson Sent: Friday, May 14, 2010 5:25 AM To: r-help@r-project.org Subject: [R] Fwd: nonlinearity and interaction [posted this at 9:25 and still hasn't appeared on the list at 13:26] I have the following set-up. 6 values of a continuous variable (let's say light intensity) are presented to a system. The input is presented as a random series of blocks lasting (say) 5 sec each. ---- ---- ---- etc ---- time -> The output is measured and sampled at say 10 samples/sec. Please ignore the fact that this is a time series and don't suggest things like ar() and arima(). I have looked at the autocorrelation function of the output and it is an amazing spike at a lag of zero and zilch elsewhere. Call the input x and the output y. I can find the relationship between x and y by fit<-lm(y~x) coef(fit) tells me the line that best fits x vs y (as shown in the plot of the 6 values of x vs the mean values of y at those values). ****Question: Suppose that the system is nonlinear such that the response to the sequence 0,2 is not the same as the response to 2, 0 -- it is not just a change of the response by the same amount. Or nonlinear in other weird ways (I don't just mean simple things like y~x^2). I am thinking that a way to characterise this might be to pretend that x is not a continuous variable and to represent it with 5 indicator variables. And then interactions between them would tell me about nonlinear effects? e.g. lm(y~ d1 + d2 + d3 + d4 + d5 + d1*d2) etc Does this make any sense? If so, please suggest a good way to go about this; how to set up the dummy variables and how to interpret the results. Ideally, the same lm() fit would tell me about the linear effect y~x and the nonlinearities. Both sorts of effect will co-exist. Thanks very much for any help! Bill ______________________________________________ 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. ______________________________________________ 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.
