# [R] Plot different regression models on one graph

kMan kchamberln at gmail.com
Mon Feb 15 03:31:35 CET 2010

```Peter wrote:
>You like to live dangerously.

Clue me in, Professor.

Sincerely,
KeithC.

-----Original Message-----
From: Peter Ehlers [mailto:ehlers at ucalgary.ca]
Sent: Sunday, February 14, 2010 6:49 PM
To: kMan
Cc: 'David Winsemius'; 'Rhonda Reidy'; r-help at 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: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
>
> 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)
>>>
>>>
>>>
>>> Rhonda Reidy
>>>
>
> --
> Peter Ehlers
> University of Calgary
>
>
>
>

--
Peter Ehlers
University of Calgary

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