On 13-08-21 05:17 PM, Rolf Turner wrote: > > > Thott about this a bit more and have now decided that I don't understand > after all. > > Doesn't > > glm(1/y~x,family=gaussian(link="inverse")) > > fit the model > > 1/y ~ N(1/(a+bx), sigma^2) > > whereas what the OP wanted was > > y ~ N(x/(a+x),sigma^2) ???
I goofed slightly. y ~ 1/x with inverse link gives 1/y = a + b*(1/x) y = 1/(a+b*(1/x)) = x/(a*x+b) Hmmm. Is there an offset trick we can use? y = x/(a+x) 1/y = (a+x)/x 1/y = (a/x) + 1 1/y = a*(1/x) + 1 So I *think* we want glm(y~1/x+0+offset(1),family=gaussian(link="inverse")) I'm forwarding this back to r-help. > > I can't see how these models can be equivalent. What am I missing? > > cheers, > > Rolf > > > > On 22/08/13 03:49, Ben Bolker wrote: >> Rolf Turner <rolf.turner <at> xtra.co.nz> writes: >> >>> On 21/08/13 11:23, Ye Lin wrote: >>>> T >>>> hanks for your insights Rolf! The model I want to fit is y=x/a+x with >>>> no intercept, so I transformed it to 1/y=1+a/x as they are the same. >>> For crying out loud, they are ***NOT*** the same. The equations y = >>> x/(a+x) and >>> 1/y = 1 + a/x are indeed algebraically identical, but if an "error" or >>> "noise" term is added >>> to each then then the nature of the error term is vastly different. It >>> is the error or >>> noise term that is of central concern in a statistical context. >>> >>> cheers, >>> >>> Rolf >> >> For what it's worth this model can also be fitted (without messing >> up the error structure) via >> >> glm(1/y~x,family=gaussian(link="inverse")) >> >> Although you may not get the parameters in exactly the form you >> want. >> >> ______________________________________________ >> 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. >> > ______________________________________________ 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.
