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Simon Pickett

2007-Apr-19 11:33 UTC

[R] general question about plotting multiple regression results

Hi all,

I have been bumbling around with r for years now and still havent come up
with a solution for plotting reliable graphs of relationships from a
linear regression.

Here is an example illustrating my problem

1.I do a linear regression as follows

summary(lm(n.day13~n.day1+ffemale.yell+fmale.yell+fmale.chroma,data=surv))

which gives some nice sig. results

Coefficients:
             Estimate Std. Error t value Pr(>|t|)
(Intercept)  -0.73917    0.43742  -1.690 0.093069 .
n.day1        1.00460    0.05369  18.711  < 2e-16 ***
ffemale.yell  0.22419    0.06251   3.586 0.000449 ***
fmale.yell    0.25874    0.06925   3.736 0.000262 ***
fmale.chroma  0.23525    0.11633   2.022 0.044868 *

2. I want to plot the effect of "ffemale.yell", "fmale.yell"
and
"fmale.chroma" on my response variable.

So, I either plot the raw values (which is fine when there is a very
strong relationship) but what if I want to plot the effects from the
model?

In this case I would usually plot the fitted values values against the raw
values of x... Is this the right approach?

fit<-fitted(lm(n.day13~n.day1+ffemale.yell+fmale.yell+fmale.chroma,data=fsurv1))

plot(fit~ffemale.yell)

#make a dummy variable across the range of x
x<-seq(from=min(fsurv1$ffemale.yell),to=max(fsurv1$ffemale.yell), length=100)

#get the coefficients and draw the line
co<-coef(lm(fit~ffemale.yell,data=fsurv1))
y<-(co[2]*x)+co[1]
lines(x,y, lwd=2)

This often does the trick but for some reason, especially when my model
has many terms in it or when one of the independent variables is only
significant when the other independent variables are in the equation, it
gives me strange lines.

Please can someone show me the light?

Thanks in advance,

Simon.






Simon Pickett
PhD student
Centre For Ecology and Conservation
Tremough Campus
University of Exeter in Cornwall
TR109EZ
Tel 01326371852

Chuck Cleland

2007-Apr-19 12:08 UTC

head link

[R] general question about plotting multiple regression results

Simon Pickett wrote:> Hi all,
> 
> I have been bumbling around with r for years now and still havent come up
> with a solution for plotting reliable graphs of relationships from a
> linear regression.
> 
> Here is an example illustrating my problem
> 
> 1.I do a linear regression as follows
> 
> summary(lm(n.day13~n.day1+ffemale.yell+fmale.yell+fmale.chroma,data=surv))
> 
> which gives some nice sig. results
> 
> Coefficients:
>              Estimate Std. Error t value Pr(>|t|)
> (Intercept)  -0.73917    0.43742  -1.690 0.093069 .
> n.day1        1.00460    0.05369  18.711  < 2e-16 ***
> ffemale.yell  0.22419    0.06251   3.586 0.000449 ***
> fmale.yell    0.25874    0.06925   3.736 0.000262 ***
> fmale.chroma  0.23525    0.11633   2.022 0.044868 *
> 
> 2. I want to plot the effect of "ffemale.yell",
"fmale.yell" and
> "fmale.chroma" on my response variable.
> 
> So, I either plot the raw values (which is fine when there is a very
> strong relationship) but what if I want to plot the effects from the
> model?
> 
> In this case I would usually plot the fitted values values against the raw
> values of x... Is this the right approach?
> 
>
fit<-fitted(lm(n.day13~n.day1+ffemale.yell+fmale.yell+fmale.chroma,data=fsurv1))
> 
> plot(fit~ffemale.yell)
> 
> #make a dummy variable across the range of x
> x<-seq(from=min(fsurv1$ffemale.yell),to=max(fsurv1$ffemale.yell),
length=100)
> 
> #get the coefficients and draw the line
> co<-coef(lm(fit~ffemale.yell,data=fsurv1))
> y<-(co[2]*x)+co[1]
> lines(x,y, lwd=2)
> 
> This often does the trick but for some reason, especially when my model
> has many terms in it or when one of the independent variables is only
> significant when the other independent variables are in the equation, it
> gives me strange lines.
> 
> Please can someone show me the light?
  Have a close look at John Fox's effects package.  For example, the
following seems to be the kind of thing you want:

fm <- lm(n.day13 ~ n.day1 + ffemale.yell + fmale.yell +
                   fmale.chroma, data=fsurv1)

library(effects)

eff.fm <- effect("ffemale.yell", fm)

plot(eff.fm)
> Thanks in advance,
> 
> Simon.
> 
> Simon Pickett
> PhD student
> Centre For Ecology and Conservation
> Tremough Campus
> University of Exeter in Cornwall
> TR109EZ
> Tel 01326371852
> 
> ______________________________________________
> R-help at stat.math.ethz.ch 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. 
-- 
Chuck Cleland, Ph.D.
NDRI, Inc.
71 West 23rd Street, 8th floor
New York, NY 10010
tel: (212) 845-4495 (Tu, Th)
tel: (732) 512-0171 (M, W, F)
fax: (917) 438-0894

Liaw, Andy

2007-Apr-19 12:32 UTC

head link

[R] general question about plotting multiple regression results [Broadcast]

I suspect you'll greatly benefit a read of Prof. Fox's book(s) on
regression models, as well as making use of his "car" package.  You
may
want to read up on partial residual plots and partial regression plots.

Andy

From: Simon Pickett> 
> Hi all,
> 
> I have been bumbling around with r for years now and still 
> havent come up with a solution for plotting reliable graphs 
> of relationships from a linear regression.
> 
> Here is an example illustrating my problem
> 
> 1.I do a linear regression as follows
> 
> summary(lm(n.day13~n.day1+ffemale.yell+fmale.yell+fmale.chroma
> ,data=surv))
> 
> which gives some nice sig. results
> 
> Coefficients:
>              Estimate Std. Error t value Pr(>|t|)
> (Intercept)  -0.73917    0.43742  -1.690 0.093069 .
> n.day1        1.00460    0.05369  18.711  < 2e-16 ***
> ffemale.yell  0.22419    0.06251   3.586 0.000449 ***
> fmale.yell    0.25874    0.06925   3.736 0.000262 ***
> fmale.chroma  0.23525    0.11633   2.022 0.044868 *
> 
> 2. I want to plot the effect of "ffemale.yell",
"fmale.yell"
> and "fmale.chroma" on my response variable.
> 
> So, I either plot the raw values (which is fine when there is 
> a very strong relationship) but what if I want to plot the 
> effects from the model?
> 
> In this case I would usually plot the fitted values values 
> against the raw values of x... Is this the right approach?
> 
> fit<-fitted(lm(n.day13~n.day1+ffemale.yell+fmale.yell+fmale.ch
> roma,data=fsurv1))
> 
> plot(fit~ffemale.yell)
> 
> #make a dummy variable across the range of x 
> x<-seq(from=min(fsurv1$ffemale.yell),to=max(fsurv1$ffemale.yel
> l), length=100)
> 
> #get the coefficients and draw the line
> co<-coef(lm(fit~ffemale.yell,data=fsurv1))
> y<-(co[2]*x)+co[1]
> lines(x,y, lwd=2)
> 
> This often does the trick but for some reason, especially 
> when my model has many terms in it or when one of the 
> independent variables is only significant when the other 
> independent variables are in the equation, it gives me strange lines.
> 
> Please can someone show me the light?
> 
> Thanks in advance,
> 
> Simon.
> 
> 
> 
> 
> 
> 
> Simon Pickett
> PhD student
> Centre For Ecology and Conservation
> Tremough Campus
> University of Exeter in Cornwall
> TR109EZ
> Tel 01326371852
> 
> ______________________________________________
> R-help at stat.math.ethz.ch 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.
> 
> 
> 

------------------------------------------------------------------------------
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Thomas Lumley

2007-Apr-19 14:37 UTC

head link

[R] general question about plotting multiple regression results

On Thu, 19 Apr 2007, Simon Pickett wrote:
> Hi all,
>
> I have been bumbling around with r for years now and still havent come up
> with a solution for plotting reliable graphs of relationships from a
> linear regression.
termplot() does this for a range of regression models (without interaction 
terms). The "effects" package does it better for linear regression
models.

 	-thomas

> Here is an example illustrating my problem
>
> 1.I do a linear regression as follows
>
> summary(lm(n.day13~n.day1+ffemale.yell+fmale.yell+fmale.chroma,data=surv))
>
> which gives some nice sig. results
>
> Coefficients:
>             Estimate Std. Error t value Pr(>|t|)
> (Intercept)  -0.73917    0.43742  -1.690 0.093069 .
> n.day1        1.00460    0.05369  18.711  < 2e-16 ***
> ffemale.yell  0.22419    0.06251   3.586 0.000449 ***
> fmale.yell    0.25874    0.06925   3.736 0.000262 ***
> fmale.chroma  0.23525    0.11633   2.022 0.044868 *
>
> 2. I want to plot the effect of "ffemale.yell",
"fmale.yell" and
> "fmale.chroma" on my response variable.
>
> So, I either plot the raw values (which is fine when there is a very
> strong relationship) but what if I want to plot the effects from the
> model?
>
> In this case I would usually plot the fitted values values against the raw
> values of x... Is this the right approach?
>
>
fit<-fitted(lm(n.day13~n.day1+ffemale.yell+fmale.yell+fmale.chroma,data=fsurv1))
>
> plot(fit~ffemale.yell)
>
> #make a dummy variable across the range of x
> x<-seq(from=min(fsurv1$ffemale.yell),to=max(fsurv1$ffemale.yell),
length=100)
>
> #get the coefficients and draw the line
> co<-coef(lm(fit~ffemale.yell,data=fsurv1))
> y<-(co[2]*x)+co[1]
> lines(x,y, lwd=2)
>
> This often does the trick but for some reason, especially when my model
> has many terms in it or when one of the independent variables is only
> significant when the other independent variables are in the equation, it
> gives me strange lines.
>
> Please can someone show me the light?
>
> Thanks in advance,
>
> Simon.
>
>
>
>
>
>
> Simon Pickett
> PhD student
> Centre For Ecology and Conservation
> Tremough Campus
> University of Exeter in Cornwall
> TR109EZ
> Tel 01326371852
>
> ______________________________________________
> R-help at stat.math.ethz.ch 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.
>
Thomas Lumley			Assoc. Professor, Biostatistics
tlumley at u.washington.edu	University of Washington, Seattle

John Fox

2007-Apr-19 21:20 UTC

head link

[R] general question about plotting multiple regression results

Dear Thomas and Simon,

On Thu, 19 Apr 2007 07:37:28 -0700 (PDT)
 Thomas Lumley <tlumley at u.washington.edu>
wrote:> On Thu, 19 Apr 2007, Simon Pickett wrote:
> 
> > Hi all,
> >
> > I have been bumbling around with r for years now and still havent
> come up
> > with a solution for plotting reliable graphs of relationships from
> a
> > linear regression.
> 
> termplot() does this for a range of regression models (without
> interaction 
> terms). The "effects" package does it better for linear
regression
> models.
> 
>  	-thomas
>
The effects package also works for generalized linear models (which, I
suppose, are arguably linear regression models).

Regards,
 John
 > 
> > Here is an example illustrating my problem
> >
> > 1.I do a linear regression as follows
> >
> >
>
summary(lm(n.day13~n.day1+ffemale.yell+fmale.yell+fmale.chroma,data=surv))> >
> > which gives some nice sig. results
> >
> > Coefficients:
> >             Estimate Std. Error t value Pr(>|t|)
> > (Intercept)  -0.73917    0.43742  -1.690 0.093069 .
> > n.day1        1.00460    0.05369  18.711  < 2e-16 ***
> > ffemale.yell  0.22419    0.06251   3.586 0.000449 ***
> > fmale.yell    0.25874    0.06925   3.736 0.000262 ***
> > fmale.chroma  0.23525    0.11633   2.022 0.044868 *
> >
> > 2. I want to plot the effect of "ffemale.yell",
"fmale.yell" and
> > "fmale.chroma" on my response variable.
> >
> > So, I either plot the raw values (which is fine when there is a
> very
> > strong relationship) but what if I want to plot the effects from
> the
> > model?
> >
> > In this case I would usually plot the fitted values values against
> the raw
> > values of x... Is this the right approach?
> >
> >
>
fit<-fitted(lm(n.day13~n.day1+ffemale.yell+fmale.yell+fmale.chroma,data=fsurv1))> >
> > plot(fit~ffemale.yell)
> >
> > #make a dummy variable across the range of x
> > x<-seq(from=min(fsurv1$ffemale.yell),to=max(fsurv1$ffemale.yell),
> length=100)
> >
> > #get the coefficients and draw the line
> > co<-coef(lm(fit~ffemale.yell,data=fsurv1))
> > y<-(co[2]*x)+co[1]
> > lines(x,y, lwd=2)
> >
> > This often does the trick but for some reason, especially when my
> model
> > has many terms in it or when one of the independent variables is
> only
> > significant when the other independent variables are in the
> equation, it
> > gives me strange lines.
> >
> > Please can someone show me the light?
> >
> > Thanks in advance,
> >
> > Simon.
> >
> >
> >
> >
> >
> >
> > Simon Pickett
> > PhD student
> > Centre For Ecology and Conservation
> > Tremough Campus
> > University of Exeter in Cornwall
> > TR109EZ
> > Tel 01326371852
> >
> > ______________________________________________
> > R-help at stat.math.ethz.ch 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.
> >
> 
> Thomas Lumley			Assoc. Professor, Biostatistics
> tlumley at u.washington.edu	University of Washington, Seattle
> 
> ______________________________________________
> R-help at stat.math.ethz.ch 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.
--------------------------------
John Fox
Department of Sociology
McMaster University
Hamilton, Ontario, Canada
http://socserv.mcmaster.ca/jfox/

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