similar to: Direction and scaling of cumulative distribution in ecdfplot

Is there an easy way to "thin" a lattice plot? I often create plots from large data sets, and use the "pdf" command to save them to a file, but the resulting files can be huge, because every point in the underlying dataset is rendered in the plot, even though it isn't possible to see that much detail. For example: require(Hmisc) x <- rnorm(1e6)

I have been using the ECDF function in the Hmisc package to produce cumulative distribution function plots. The problem is that for small datasets the steps "look bad" (not my characterization but from the client). Is there a way to get the same information but smoothed? I have tried the densityplot (lattice), which gives a smoothed line, but this does not give the cumulative density.

Hi, I am trying to add text to the bottom of a lattice bwplot with multiple panels. I would like to add a label below each boxplot, but the labels do not come from the data. I've tried the following, code: f1 <- c(rep(c(rep("a", 3), rep("b", 3), rep("c", 3)), 2)) f2 <- c(rep("A", 9), rep("B", 9)) dv <- c(0.9, 0.8, 0.85, 0.6, 0.65,

Dear R-Experts, I was wondering how to fit a cumulative gaussian to a set of empirical data using R. On the R website as well as in the mail archives, I found a lot of help on how to fit a normal density function to empirical data, but unfortunately no advice on how to obtain reasonable estimates of m and sd for a gaussian ogive function. Specifically, I have data from a psychometric function

Dear R List, I have found that R handles a remarkable number of analytic tasks that are overlooked by major commercial packages. However, I have not found an R package that handles MaxDiff Scaling - does one exist? Thanks in Advance, Stephen Brand

Folks, I have a small dataset of counts of recoveries on defaulted loans: recoveries<-structure(c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 0, 0, 0, 0, 0, 4, 0, 1, 2, 2, 12), .Dim = c(11L, 2L), .Dimnames = list( NULL, c("pcts", "counts"))) Here is the data in columnar form: pcts counts [1,] 0.0 0 [2,] 0.1 0 [3,] 0.2 0 [4,] 0.3

Hi I have a general statistics question on calculating confidence interval of log transformed data. I log transformed both x and y, regressed the transformed y on transformed x: lm(log(y)~log(x)), and I get the following relationship: log(y) = alpha + beta * log(x) with se as the standard error of residuals My question is how do I calculate the confidence interval in the original scale of x

Dear guRus: I am using lmer for a mixed model that includes a random intercept for a set of effects that have the same distribution, Normal(0, sig2b). This set of effects is of variable size, so I am using an as.formula statement to create the formula for lmer. For example, if the set of random effects has dimension 8, then the lmer call is: Zs<-

This should be simple but I am struggling. I like to easily switch in xyplot between a linear or logarithmic y-axis by setting a logical flag logY to False or True. This switch changes the scales argument of xyplot. I found out that the original two-dimentional data (Conc vs Time in my case) are converted to log10(Conc) if log=TRUE in scales, but it appears that functions like panel.curve need to

Dear R-users, I am using R version 2.10.1 under windows. In a barplot, I want to mark one of the bars with a special border color. For example: barplot(c(3, 7, 11), border = c(NA, "red", NA)) But how to do this when the bars are stacked? for example: barplot(matrix(1:6, ncol=3)) # border of second bar (i.e. the one with total height = 7) should be red again, I try: barplot(matrix(1:6,

Hi all, I'm trying to get a vertical line at a specific point in a densityplot. abline seems to be what's required, but it doesn't align itself to the scale used in the plot. example: library(lattice) x<-rnorm(100) plot.new() densityplot(x) abline(v=0) ----- The line seems to use some other coordinate system. What kind of call do I use to make abline use the graph's

Hi All, I'm trying to add a 'rug' representation of my data to a plot created with densityplot(). While I can do this in the simple case, I can't do it properly when I include the "groups" argument. I have an example below. I am running a reasonably new version of R. print(sessionInfo()) R version 2.12.0 Patched (2010-11-07 r53537) Platform: i686-pc-linux-gnu

Hi, I have the following script: ---- t.R --- grafica <- function() { v <- read.csv('preprocessed/komolongma.ece.uprm.edu.active',sep=',') x <- as.ts(v$active) bitmap(file="output.png") densityplot(~x,col='blue',main='Density Plot') dev.off() } grafica() ---- t.R --- When I "sourced" it from R prompt, it quietly runs.

Hi Dear R users, For a pair plotting, usaully we use par( ) function. Apparently it does not work anywhere. I want to have 3 plots in a single figure, like this: par(mfrow=c(3,1)) densityplot( a) densityplot(b) densityplot(c) But it does not work. How is it possible to have such a figure with densityplot( ) in a single figure? So many thanks for any help. Amir Safari

Hi folks, This is really minor but to someone not familiar with the various tentacles of the lmer package it could be really annoying. I was trying to plot the posterior density of the fixed effect parameters of a lmer model, > hr.mcmc = mcmcsamp(hr.lmer, n=50000) > densityplot(hr.mcmc, plot.points=F) There is this error, "Error in densityplot(hr.mcmc, plot.points = F) : no

Hi, I have created two separate overlapping density plots- see example code below. What I wish now to do is combine them into one figure where they sit side by side. Any help would be great! many thanks in advance, josh. ##################### thedataA <- data.frame(x1=rnorm(100,1,1),x2=rnorm(100,3,1)) #create data thedataA.m<-melt(thedataA) densityplot(~value, thedataA.m,

I'm trying to use a lattice function within a function and have problems passing the "groups" argument properly. Let's say I have a data frame d <- data.frame(x = rnorm(100), y = c("a", "b")) and want to plot variable x in a densityplot, grouped by the variable y, then I would do something like densityplot(~ x, d, groups = y) If however I wanted to

Hi I would like to superimpose group-specific densityplots on top of an overall histogram using panel.histogram and panel.densityplot. Furthermore, I would like to automatically adjust the range of the y-axis to take into account the ranges of both histogram and densityplot. This last part is where I have a problem. I believe using the prepanel argument of histogram is typically the way to go,

Hello, I'm doing some graphics for a paper and a need customize such with filled region above the density curve. My attempts I get something very near what I need, but I don't solve the problem of use semitransparent filled. Below a minimal reproducible code. Someone has any idea? require(lattice) # toy data... dt <- expand.grid(A=1:2, B=1:3, y=1:50) dt$y <- rnorm(nrow(dt), dt$B,

Hi, i have a script how i launch lattice to make a densityplot. in the script: jpeg(file="XXX.jpg") densityplot(~f_diametro+m_diametro+n_diametro, plot.points="rug", auto.key=T) dev.off() does'nt work and in R i dont have any output but if i launch by R row by row, runs correctly..... any idea?