similar to: Exponential Smoothing for ggplot2's stat_smooth()

I'm interested in creating a graphic -like- this: c <- ggplot(mtcars, aes(qsec, wt)) c + geom_point() + stat_smooth(fill="blue", colour="darkblue", size=2, alpha = 0.2) but I need to show 2 sets of bands (with different shading) using 5%, 25%, 75%, 95% limits that I specify and where the heavy blue line is the median. I don't understand how to do this with

In the example below, from http://www.ling.upenn.edu/~joseff/rstudy/summer2010_ggplot2_intro.html I'd like to make (a) the fitted line thicker and (b) change the background fill color for the confidence envelope around each fitted line to a low-alpha transparent version of the same color used for the separate fitted lines for GENDER, rather than grey for both. How can I do this?

Hi all, I am using ggplot2 and continuing to find it very useful and pretty. However, I am trying to create some graphics for publication that would be included in an MS Word document (not my choice!) in Windows Vista. The problem is that I want to use stat_smooth() to add an fitted linear model line along with its 95% confidence band, but I cannot seem to get the confidence band in a format

Hi all, Does one of you know if there is any way to combine a "nls" method in the stat_smooth of ggplot? Regards -- ------------- Benoit Boulinguiez Ph.D student Ecole de Chimie de Rennes (ENSCR) Bureau 1.20 Equipe CIP UMR CNRS 6226 "Sciences Chimiques de Rennes" Avenue du G?n?ral Leclerc CS 50837 35708 Rennes CEDEX 7 Tel 33 (0)2 23 23 80 83 Fax 33 (0)2 23 23 81 20

With ggplot2, I can plot the glm stat_smooth for binomial data when the response is binary or a two-level factor as follows: data("Donner", package="vcdExtra") ggplot(Donner, aes(age, survived)) + geom_point(position = position_jitter(height = 0.02, width = 0)) + stat_smooth(method = "glm", family = binomial, formula = y ~ x, alpha = 0.2, size=2) But how can I

Hello, I drew a simple area plot using ggplot2 using set <- read.table(file="http://www.jovian.nl/set.csv", head=1, sep=",") library(ggplot2) ggplot() + layer( data = set, mapping = aes(x = time, y = hours), geom = "area", stat="smooth", color="red" ) + layer( data = set, mapping = aes(x = time, y = hours), geom = "area",

Dear all I would like to superpose some smoothing line through boxplot in ggplot > dput(ad) structure(list(konc.f = structure(c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 6L, 6L, 6L, 7L, 7L, 7L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L,

In the change log of ggplot2, version 0.7, I find this: "* scales: any point outside of limits is dropped (this was previously the behaviour for discrete scales, but not continuous scales)" and that makes sense for some applications. But what about if I want to summarize the data with a smooth, and (a) include the effect of all values, but (b) "zoom in" on the smooth by

is it possible to map 2 plots(c and d) in a single output plot? library(ggplot2) c <- ggplot(mtcars, aes(qsec, wt)) d <- ggplot (mtcars, aes(qsec, wt)) c + stat_smooth(fill="darkgrey", colour="blue", size=2, alpha = 0.2) d + stat_smooth(fill="darkgrey", colour="red", size=2, alpha = 0.2) -- View this message in context:

Dear List, I have a wrapper function that draws a graph that I'd like to use in a vector-like manner. The for-loop version I currently use is below. library(ggplot2) data(economics) h <- 600 w <- 800 #---------------------------------------------------------- draw_metric_by_date <- function( df, i, smooth=FALSE, BASEPATH ) { mlabel <- names(df)[i] qmetric

Hi All, I just installed Cairo on R 2.5.1 on windows XP. My hope was to get to see the transparency output e.g. http://had.co.nz/ggplot2/stat_smooth.html ggplot2 - stat_smooth , which I finally managed to do. However, I find the Cairo device, which I access either through CairoWin() or Cairo(type="win"), is pretty shaky in a number of respects : - whatever portion of the Cairo

I'm trying to treat a continuous variable as discrete for plotting multiple regression lines in a scatterplot as a function of the level on the moderating variable. In the example below, there is only one regression line plotted to the whole data. I would like a separate regression line for each discrete level of the moderator. The moderator is continuous, so I'd like to treat it as

Dear list (probably Hadley), I'm trying to do a plot like the following, composed of bars and error bars: df <- data.frame(factor1=gl(2,5), factor2=gl(5,1), y=rnorm(10), err=0.1) ggplot(df, aes(x=factor1, y=y, fill=factor2)) + geom_bar(position="dodge", stat="identity") + geom_errorbar(aes(min=y-err, max=y+err), position="dodge", width=0.1) The

I get the following error when I run qplot() qplot(grade, read,data = hhm.long.m, geom = c("point", "smooth")) Error in smooth.construct.cr.smooth.spec(object, data, knots) : x has insufficient unique values to support 10 knots: reduce k. I am not sure how to tackle this problem. When I take a subsample (< 1000) than I am able to run that function but with my sample

AF_E PS_E 90.838 2.206 83.139 1.751 134.272 3.710 84.043 2.076 105.184 2.788 157.249 3.783 50.280 1.027 96.973 2.355 123.582 3.398 60.417 1.236 123.501 3.315 90.128 1.566 193.783 5.167 116.036 2.994 100.289 2.216 56.943 1.106 102.272 2.692 145.579 3.810 53.105 1.202 127.212 3.061 102.838 2.383 126.352 2.723 13.661 0.190 164.352 4.870 159.945 4.160 54.382 0.884 128.253 3.598 181.208 4.767 145.118

This is what I have now so far. p=ggplot(data = test, aes(x = YEAR, y = TOTAL, colour = TREATMENT)) + geom_point() + geom_smooth(method = "lm", se=FALSE) + facet_wrap(~COUNTRY) > p +scale_x_continuous(limits=c(1,4)) http://r.789695.n4.nabble.com/file/n3626510/graph.gif I would also like to: 1.) change the headline for the faced wraps ?high? and ?low?. Is there any other way of

Suppose that I'm working on Hadley's diamond dataset and I want to review the relationship between price, colour and carat. I might run the following: library(ggplot2) #plot scatter and add some hex binning q<-qplot(carat,price,data=diamonds, geom=c("hex"), main="Variability of Diamond Prices by Carat and Colour") #facet to get one scatter for

The following variables have the following significant relationships (x is the explanatory variable): linear, cubic, exponential, logistic. The linear relationship plots without any trouble. Cubic is the 'best' model, but it is not plotting as a smooth curve using the following code: cubic.lm<- lm(y~poly(x,3)) lines(x,predict(cubic.lm),lwd=2) How do I plot the data and the estimated

Dear r-helpers, I'm using ggplot2::ggplot to plot seven time series on the same graph. c <- ggplot(jobm, aes(y = value, x = year, colour = kind)) c + stat_smooth() This gives me a legend with colors that are not very different. Can I label the lines instead? How? _____________________________ Professor Michael Kubovy University of Virginia Department of Psychology USPS: P.O.Box

Hello all, Have some time series data stored in a data.frame, and am plotting it with ggplot2 (which is totally awesome). I have explored the documentation and mailing list archives, and I can't see any way to plot a 'smoother' that is just the K-step moving average. For example, imagine I had a data.frame called 'sleep' with 'date' as the date (from as.Date()) and