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.