ecdf

If I use the ecdfplot() function of the latticeExtra package how do I get the actual values calculated i.e. the y-values which correspond to the ~x|g input? I've been looking at ?ecdfplot but there's not discription to it. For the usual highlevel function ecdf() it works with the command plot=FALSE but this does not work for ecdfplot() . The reason I want to use ecdfplot() rather than ecdf() is that I need to calculate the ecdf() values for a grouping variable. I know I could do this handish too but I'm quite convinced that there is a highroad too. Here a small expample u <- rnorm(100,0,1)

I am trying to plot 4 ecdf functions on one plot but can't seem to figure out the proper syntax. If I have 4 functions "A, B, C, D" what would be the proper syntax in R to get them to be plotted on the same chart with different colors. Thanks! The package latticeExtra provides the function ecdfplot . library(lattice) library(latticeExtra) set.seed(42) vals <- data.frame(r1=rnorm(100)*0.5, r2=rnorm(100), r3=rnorm(100)*2) ecdfplot(~ r1 + r2 + r3, data=vals, auto.key=list(space='right') Here is one way (for three of them, works for four the same way): set.seed(42) ecdf1 <- ecdf(rnorm(100)*0.5)

I have a couple of cumulative empirical density functions which I would like to plot on top of each other in order to illustrate differences in the two curves. As was pointed out in a previous question , the function to draw the ECDF is simply plot(Ecdf()) And as I read the fine manual page, I determined that I can plot multiple ECDFs on top of each other using something like the following: require( Hmisc ) set.seed(3) g <- c(rep(1, 20), rep(2, 20)) Ecdf(c( rnorm(20), rnorm(20)), group=g) However my curves sometimes overlap a bit and can be hard to tell which is which, just like the example

I would like to get a plot that combines the density of observations and the cdf. The usual problem with that is that the scales of the two are way off. How can this be remedied, i.e., two scales be used or, alternatively, one of the data series be rescaled (preferably within ggplot, as I would like to separate computation and display of data). Here's the code so far: >dput(tmp) yields structure(list(drivenkm = c(8, 11, 21, 4, 594, 179, 19, 7, 10, 36)), .Names = "drivenkm", class = c("data.table", "data.frame" ), row.names = c(NA, -10L), .internal.selfref = <pointer: 0x223cb78>) then I do p =

I'm trying to use stat_ecdf() to plot cumulative successes as a function of a rank score created by a predictive model. #libraries require(ggplot2) require(scales) # fake data for reproducibility set.seed(123) n <- 200 df <- data.frame(model_score= rexp(n=n,rate=1:n), obs_set= sample(c("training","validation"),n,replace=TRUE)) df$model_rank <- rank(df$model_score)/n df$target_outcome <- rbinom(n,1,1-df$model_rank) # Plot Gain Chart using stat_ecdf() ggplot(subset(df,target_outcome==1),aes(x = model_rank)) + stat_ecdf(aes(colour = obs_set), size=1) + scale_x_continuous(limits=c(0,1), labels