You could use a glm with the binomial family to model that. A solution with ggplot2
library(ggplot2) ggplot(dataset, aes(x = x, y = y, weights = n)) + geom_smooth(method = "glm", family = binomial) geom_point() ------------------------------------------------------------------------ ---- ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest Cel biometrie, methodologie en kwaliteitszorg / Section biometrics, methodology and quality assurance Gaverstraat 4 9500 Geraardsbergen Belgium tel. + 32 54/436 185 thierry.onkel...@inbo.be www.inbo.be To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher The plural of anecdote is not data. ~ Roger Brinner The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. ~ John Tukey -----Oorspronkelijk bericht----- Van: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] Namens r2L Verzonden: vrijdag 4 september 2009 13:04 Aan: r-help@r-project.org Onderwerp: [R] plot positive predictive values Hi, I'm trying to fit a smooth line in a plot(y ~ x) graph. x is continuous variable y is a proportion of success in sub-samples, 0 <= y <= 1, from a Monte Carlo simulation. For each x there may be several y-values from different runs. Each run produces several sub-samples, where "0" mean no success in any sub- sample, "0.5" means success in half of the sub-samples, and "1" means success in all sub-samples, and so on. As x is increased, the y-value approaches 1, and may reach it; it can, of course never bypass it. >From my understanding of the data at hand, each point along the x-axis has its own beta-distribution of the y-values, then as 0 <= y <= 1, which shift gradually through distributions similar to curve(dbeta(x,2, 2), add=F, col="red", xlim=c(0,1), ylim=c(0,4)) curve(dbeta(x,4, 2), add=T, col="red", xlim=c(0,1), ylim=c(0,4)) curve(dbeta(x,4, 1), add=T, col="red", xlim=c(0,1), ylim=c(0,4)) curve(dbeta(x,4, .1), add=T, col="red", xlim=c(0,1), ylim=c(0,4)) as x increases. If I plot my data using boxplot it shows also very nicely how the the data approaches 1 and variation decreases. However, my x-axis data are continuous. Is there a way to produce a regression line which would smoothly follow that trend? It may well be easier than I believe, but my head is at a full-stop... Thanks for any help! ______________________________________________ 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. Druk dit bericht a.u.b. niet onnodig af. Please do not print this message unnecessarily. Dit bericht en eventuele bijlagen geven enkel de visie van de schrijver weer en binden het INBO onder geen enkel beding, zolang dit bericht niet bevestigd is door een geldig ondertekend document. The views expressed in this message and any annex are purely those of the writer and may not be regarded as stating an official position of INBO, as long as the message is not confirmed by a duly signed document. ______________________________________________ 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.