similar to: ggplot2's geom_errorbar legend

Hello, I need to plot multiple confidence intervals for the same model parameter e.g. so for the same value of the parameter in point x_1 I would like to see four different confidence intervals so that I can compare the accuracy e.g. boot basic vs normal vs my own vs classic lm CI etc. I like very very much the plotCI implemented here: http://cran.r-project.org/web/packages/plotrix/index.html

Hello, How can I fix this? I have the latest version of R 2.13.2 and I use Mac OS X 10.7.2 > library(doBy) Loading required package: lme4 Error in dyn.load(file, DLLpath = DLLpath, ...) : function 'cholmod_l_start' not provided by package 'Matrix' Error: package 'lme4' could not be loaded > library(lme4) Error in dyn.load(file, DLLpath = DLLpath, ...) : function

Hello, Can anyone point me to an online tutorial or book containing the easiest way to do ANOVA over the result data from a 2^k*r experiment. It is not clear to me if I can pass the raw data corresponding to each experiment or just the summarized data i.e. mean, sse, std, etc. I would like to get the: - box plot showing the effect for the different factors and levels - plot showing whether there

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

Hello, This is one problem at the time :) I have a data frame df that looks like this: time partitioning_mode workload runtime 1 1 sharding query 607 2 1 sharding query 85 3 1 sharding query 52 4 1 sharding query 79 5 1 sharding query 77 6 1 sharding query 67 7 1

Dear R-list I'm working with with geom_errorbar; specifically I'm trying to reproduce the example Hadley Wickham have on http://had.co.nz/ggplot2/geom_errorbar.html (all in the button of the page) where he makes an nice plot with errorbars and then draw lines between the points. What confuses me is the 'limits' he defines for the errorbars from the se variable. First he creates

Hi all, I have run into what appears to be a bug in ggplot2; however, I am new to the ggplot syntax, so I might be missing a key element. The main issue is that I cannot get geom_abline to plot when colour is used to identify "group" in the main plot. When I remove colour, geom_abline works but the error bars have an undesirable line drawn between the left edges of the caps. Does

Hello, I have tried reading the documentation and googling for the answer but reviewing the online matches I end up more confused than before. My problem is apparently simple. I fit a glm model (2^k experiment), and then I would like to predict the response variable (Throughput) for unseen factor levels. When I try to predict I get the following error: > throughput.pred <-

Hello, I have a loop where I iterate performance data files within a folder, parse and plot them in one shot (see below). However, when executing plot_raw which invokes dev.new(..) all windows come out blank whereas if I execute each file outside of a loop then I can see the plots properly. What's wrong here? Thanks in advance, Best regards, Giovanni # given a directory name, it will

Hello, I have a data frame that looks like this (containing interarrival times): > str(df) 'data.frame': 18233 obs. of 1 variable: $ Interarrival: int 135 806 117 4 14 1 9 104 169 0 ... > head(df) Interarrival 1 135 2 806 3 117 4 4 5 14 6 1 > This corresponds to the time differences (in ms) of a poisson arrival

Hello, I bumped into the following funny use-case. I have too much data for a given plot. I have the following data frame df: > str(df) 'data.frame': 5015 obs. of 5 variables: $ n : Factor w/ 5 levels "1000","2000",..: 1 1 1 1 1 1 1 1 1 1 ... $ iter : int 10 20 30 40 50 60 70 80 90 100 ... $ Error : num 1.05e-02 1.24e-03 3.67e-04 1.08e-04

Hello, I am attending a course in Computational Statistics at ETH and in one of the assignments I am asked to prove that a time series is not autocorrelated using the R function "acf". I tried out the acf function with the given data, according to what I found here: http://landshape.org/enm/options-for-acf-in-r/ this test data does not look IID but rather shows some trends so how can I

Hello, Can anyone provide or point me to a good setup for the listings latex package that would produce nice R-syntax highlighting? I am using an example I found in internet for setting up listings like this: \lstset{ language=R, basicstyle=\scriptsize\ttfamily, commentstyle=\ttfamily\color{gray}, numbers=left, numberstyle=\ttfamily\color{red}\footnotesize, stepnumber=1, numbersep=5pt,

Hello, Suppose I have the dataset shown below. The amount of observations is too massive to get a nice geom_point and smoother on top. What I would like to do is to bin the data first. The data is indexed by Time (minutes from 1 to 120 i.e. two hours of System benchmarking). Option 1) group the data by Time i.e. minute 1, minute 2, etc and within each group create bins of N consecutive

Hello, What would be the best set of R functions to parse and transform a file? My file looks as shown below. I would like to plot this data and I need to parse it into a single data frame that sorts of "transposes the data" with the following structure: > df <- data.frame(n=c(1,1,2,2),iter=c(1,2,1,2),step=as.factor(c('Step 1', 'Step2', 'Step 1',

Hello, I have one replication (r=1 of the 2^k*r) of a 2^k experimental design in the context of performance analysis i.e. my response variables are Throughput and Response Time. I use the "aov" function and the results look ok: > str(throughput) 'data.frame': 286 obs. of 7 variables: $ Time : int 6 7 8 9 10 11 12 13 14 15 ... $ Throughput : int 42 44 33 41

Hello, I used R a lot one year ago and now I am a bit rusty :) I have my raw data which correspond to the list of runtimes per minute (minute "1" "2" "3" in two database modes "sharding" and "query" and two workload types "query" and "refresh") and as a list of char arrays that looks like this: > str(data) List of 122 $ :

Hi, Dummy data script and scripts are attached below. I would like to change the plot to look like this:

Hello, I have just ordered the "ggplot2: Elegant Graphics for Data Analysis (Use R)" but while it arrives :) can anyone please show me how to setup and add a simple legend to a ggplot? This is my use case, I need a legend showing CI "Classic", "Own bootstrap", "R bootstrap": library(ggplot2) e <- 1 p <- 1 x <- 1:S y <- rep(betas[p],S) data

Hello, I managed to "linearize" my LDA decision boundaries now I would like to call abline three times but be able to specify the exact x range. I was reading the doc but it doesn't seem to support this use-case? are there alternatives. The reason why I use abline is because I first call plot to plot all the three datasets and then call abline to "append" these decision