Thank you! Your codes produced plots that was fairly close to what I wanted. I am not familiar with ggplot2, so I need to study what exactly each code is doing. Actually, I also want to plot the observed (raw) data points (not just confidence intervals shown by ggplot, assuming that colored portion is confidence intervals). I actually produced the one I wanted before, but my hard drive died and lost all R codes I wrote are lost. I have posted an example plot I created before using coplot function (downloadable in PowerPoint format from http://laurentian.academia.edu/KiyoshiSasaki/Miscellaneous ).
Could you please show me how to plot raw data points in those ggplots? And, can anyone help me reproduce that plot I made before using coplot? I just cannot figure out how I did to make that plot (I spent several days...). Thank you for taking your time to help me out. Sincerely, Kiyoshi ________________________________ From: Michael Friendly <frien...@yorku.ca> Cc: "r-help@r-project.org" <r-help@r-project.org> Sent: Sunday, September 22, 2013 7:46:04 PM Subject: Re: Conditioning plots (wth coplot function) with logistic regression curves On 9/21/2013 11:12 PM, Kiyoshi Sasaki wrote: > I have been trying to produce a conditional plot using coplot function > (http://stat.ethz.ch/R-manual/R-devel/library/graphics/html/coplot.html) for > a binary response ("Presence" in my case) variable and one continuous > variable ("Overstory") given a specific levels of the other continuous > variable ("Ivy"). But, my codes produces an overlapping graph. Also, I want > to use three equal intervals for "Ivy" (i.e.,33.3 each), but I could not > figure out how. Here is my data and codes I used: > If you feel it hurts because you are banging your head into a wall trying t[[elided Yahoo spam]] Instead, you might consider using ggplot2, which handles this case nicely, as far as I can tell from your description. But first a due diligence caveat: Say you come to me for consulting on this little plotting question. I look at your data frame, dat, and I see there are a number of other variables that might explain Presence, so maybe the marginal plot that ignores them could be misleading, e.g., any of Moist, Leaf, Prey, ... could moderate the relation between overstory and presence, but you won't see that in a marginal plot. Here are a couple of quick ggplot examples, plotting classes of Ivy in the same plot frame, and on the probability scale. library(ggplot2) ggplot(dat, aes(Overstory, Presence), color=Ivy>50 ) + stat_smooth(method="glm", family=binomial, formula= y~x, alpha=0.3, aes(fill=Ivy>50)) dat$Ivy3 <- factor(cut(dat$Ivy,3)) plt <- ggplot(dat, aes(Overstory, Presence), color=Ivy3 ) + stat_smooth(method="glm", family=binomial, formula= y~x, alpha=0.3, aes(fill=Ivy3)) plt If you want separate panels, try something like plt + facet_grid(. ~ Ivy3) For plots on the logit scale, try something like logit <- function(p) log(p)/log(1-p), then plt + coord_trans(y="logit") -- Michael Friendly Email: friendly AT yorku DOT ca Professor, Psychology Dept. & Chair, Quantitative Methods York University Voice: 416 736-2100 x66249 Fax: 416 736-5814 4700 Keele Street Web: http://www.datavis.ca Toronto, ONT M3J 1P3 CANADA [[alternative HTML version deleted]]
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