Peter wrote: >You like to live dangerously. Clue me in, Professor.
Sincerely, KeithC. -----Original Message----- From: Peter Ehlers [mailto:ehl...@ucalgary.ca] Sent: Sunday, February 14, 2010 6:49 PM To: kMan Cc: 'David Winsemius'; 'Rhonda Reidy'; r-help@r-project.org Subject: Re: [R] Plot different regression models on one graph kMan wrote: > I would use all of the data. If you want to "drop" one, control for it in > the model & sacrifice a degree of freedom. You like to live dangerously. -Peter Ehlers > > Why the call to poly() by the way? > > KeithC. > > -----Original Message----- > From: Peter Ehlers [mailto:ehl...@ucalgary.ca] > Sent: Saturday, February 13, 2010 1:35 PM > To: David Winsemius > Cc: Rhonda Reidy; r-help@r-project.org > Subject: Re: [R] Plot different regression models on one graph > > Rhonda: > > As David points out, a cubic fit is rather speculative. I think that one > needs to distinguish two situations: 1) theoretical justification for a > cubic model is available, or 2) you're dredging the data for the "best" fit. > Your case is the second. That puts you in the realm of EDA (exploratory data > analysis). You're free to fit any model you wish, but you should assess the > value of the model. One convenient way is to use the influence.measures() > function, which will show that points 9 and 10 in your data have a strong > influence on your cubic fit (as, of course, your eyes would tell you). A > good thing to do at this point is to fit 3 more cubic models: > 1) omitting point 9, 2) omitting point 10, 3) omitting both. > > Try it and plot the resulting fits. You may be surprised. > > Conclusion: Without more data, you can conclude nothing about a > non-straightline fit. > > (And this hasn't even addressed the relative abundance of x=0 data.) > > -Peter Ehlers > > David Winsemius wrote: >> On Feb 13, 2010, at 1:35 PM, Rhonda Reidy wrote: >> >>> The following variables have the following significant relationships >>> (x is the explanatory variable): linear, cubic, exponential, logistic. >>> The linear relationship plots without any trouble. >>> >>> Cubic is the 'best' model, but it is not plotting as a smooth curve >>> using the following code: >>> >>> cubic.lm<- lm(y~poly(x,3)) >> Try: >> >> lines(0:80, predict(cubic.lm, data.frame(x=0:80)),lwd=2) >> >> But I really must say the superiority of that relationship over a >> linear one is far from convincing to my eyes. Seems to be caused by >> one data point. I hope the quotes around "best" mean tha tyou have the > same qualms. >> >>> lines(x,predict(cubic.lm),lwd=2) >>> >>> How do I plot the data and the estimated curves for all of these >>> regression models in the same plot? >>> >>> x <- c(62.5,68.5,0,52,0,52,0,52,23.5,86,0,0,0,0,0,0,0,0,0,0) >>> >>> y <- >>> c(0.054,0.055,0.017,0.021,0.020,0.028,0.032,0.073,0.076,0.087,0.042,0 >>> .042,0.041,0.045,0.021,0.018,0.017,0.018,0.028,0.022) >>> >>> >>> Thanks in advance. >>> >>> Rhonda Reidy >>> > > -- > Peter Ehlers > University of Calgary > > > > -- Peter Ehlers University of Calgary ______________________________________________ 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.
