Dear Thomas and Simon, On Thu, 19 Apr 2007 07:37:28 -0700 (PDT) Thomas Lumley <[EMAIL PROTECTED]> 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 > > > > ______________________________________________ > > [email protected] 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 > [EMAIL PROTECTED] University of Washington, Seattle > > ______________________________________________ > [email protected] 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/ ______________________________________________ [email protected] 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.
