[R] Plot different regression models on one graph
ehlers at ucalgary.ca
Mon Feb 15 02:48:36 CET 2010
> 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.
> Why the call to poly() by the way?
> -----Original Message-----
> From: Peter Ehlers [mailto:ehlers at ucalgary.ca]
> Sent: Saturday, February 13, 2010 1:35 PM
> To: David Winsemius
> Cc: Rhonda Reidy; r-help at r-project.org
> Subject: Re: [R] Plot different regression models on one graph
> 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))
>> 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.
>>> 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 <-
>>> Thanks in advance.
>>> Rhonda Reidy
> Peter Ehlers
> University of Calgary
University of Calgary
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