Sorry that was a typo when I said 'resposnse'... I meant predictor. I want to fit lm(y ~ log(x)) and plot the line with confidence intervals on a log="x" plot so that I can see the real units of x rather than the log(x) units. I can't get the real line using predict.lm() without removing the log() from the formula. Thanks again.
Quoting Peter Dalgaard <[EMAIL PROTECTED]>: > Richard Chandler <[EMAIL PROTECTED]> writes: > > > Thanks for the reply though I don't think your suggestion worked. > I > > have found a way to get the correct line though it is not > > convenient. > > > > x <- 1:100 > > y <- 1:100 > > plot(y ~ x, log="x") > > > > #The only way I can get the correct line is to drop the log(): > > abline(lm(y ~ x), untf=T, lwd=2) #or > > lines(x, predict(lm(y ~ x)), col=2) > > > > #Neither of these work > > abline(lm(y ~ log10(x))) #or > > abline(lm(y ~ log10(x)), untf=T) > > > > What I really would like to do is plot fitted lines and 95% > > confidence intervals using predict.lm, as in shown in the > example, > > but when the predictor is log transformed and log="x". I can't > figure > > out how to do this without removing the log() from the response > part > > of the formula and this isn't helpful because I'm generally > trying to > > give predict() a fitted object rather than a lm() formula. I > still > > think I'm probably missing something simple but are there any > other > > suggestions? Thanks. > > > > First decide what you really want. I see log() hopping all over > the > place. Is it on the response or the predictor? Do you want a > straight > line on an x-logged plot or an x-logged plot of a straight line? > Do > you intend to fit y~x or y~log(x) ? > > > > > Richard > > > > > > Quoting Peter Dalgaard <[EMAIL PROTECTED]>: > > > > > Richard Chandler <[EMAIL PROTECTED]> writes: > > > > > > > Hello, > > > > > > > > I'm trying to plot a fitted lm() line on a plot when the one > > > > explanatory variable is log transformed and log="x". I get > > > different > > > > lines using abline and predict.lm(). > > > > > > > > #Example > > > > x <- 1:100 > > > > y <- rnorm(100) > > > > plot(y ~ x, log="x") > > > > abline(lm(y ~ log(x))) > > > > lines(x, predict(lm(y ~ log(x))), lwd=2) > > > > > > > > I'm sure I'm missing something but could someone tell me > which > > > line is > > > > correct? Thanks. > > > > > > Base 10 is what you're missing. > > > > > > The latter form is agnostic with respect to base, the former > is > > > not > > > (since the fitted values are the same, but regression > coefficients > > > differ). So you need to know to use abline(lm(y ~ log10(x))). > > > > > > You don't really notice which kind of log is being used until > you > > > look > > > at par(usr) for a plot with logged axes. > > > > > > -- > > > O__ ---- Peter Dalgaard Øster Farimagsgade 5, > > > Entr.B > > > c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. > K > > > (*) \(*) -- University of Copenhagen Denmark Ph: > (+45) > > > 35327918 > > > ~~~~~~~~~~ - ([EMAIL PROTECTED]) FAX: > (+45) > > > 35327907 > > > > > > > > > -- > > Richard Chandler, M.S. candidate > > Department of Natural Resources Conservation > > UMass Amherst > > (413)545-1237 > > > > -- > O__ ---- Peter Dalgaard Øster Farimagsgade 5, > Entr.B > c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K > (*) \(*) -- University of Copenhagen Denmark Ph: (+45) > 35327918 > ~~~~~~~~~~ - ([EMAIL PROTECTED]) FAX: (+45) > 35327907 > -- Richard Chandler, M.S. candidate Department of Natural Resources Conservation UMass Amherst (413)545-1237 ______________________________________________ [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
