similar to: Definition of Hinges in Boxplot

I have read and reread the boxplot and the boxplot stats page, and I still cannot understand how and what boxplot shows. I realize that this might be due to me not knowing enough statistics, but anyway... First, how does boxplot determine the size of the box? And is the line inside the box the mean or the median (or something completely different?) And how does it determine how long out the

Any help with this problem would be greatly appreciated: I need to produce a custom plot i haven't come across in R. Basically, I want to show means, 1st standard deviation and 5th and 95th percentiles visually, using something resembling a boxplot. Is it possible to completely customize a boxplot so that it shows means as the bar (instead of, not as well as medians), standard deviations at

Hi! My name is Martin and I have a problem concerning the boxplot function in R. I want my boxes to be limited by the 1st and 3rd quartile and NOT the 'hinges' values that are the default setting in R. Do anyone knows if there is any command that I could do to change this default setting? Sincerely Martin Olofsson

Hello, I faced something I can't understand. When I use boxplot.stats(1:10) and quantiles(1:10) the results are different for 25% and 75%: > boxplot.stats(1:10) $stats [1] 1.0 3.0 5.5 8.0 10.0 > quantile(1:10) 0% 25% 50% 75% 100% 1.00 3.25 5.50 7.75 10.00 Actually, I expected the value 3 for 25% and 8 for 75% as results of quantile(1:10). Can you please explain me

Hi, I have a simple question about quartiles in R, especially how they are calculated using the boxplot. Quartiles (.25 and .75) in boxplot are different from the summary function and also don't match with the 9 types in the quantile function. See attachment for details. Can you give me the details on how the boxplot function does calculate these values? Cheers, Rene Brinkhuis

PR #7690 points out that if the confidence intervals (+/-1.58 IQR/sqrt(n)) in a boxplot with notch=TRUE are larger than the hinges -- which is most likely to happen for small n and asymmetric distributions -- the resulting plot is ugly, e.g.: set.seed(1001) npts <- 5 X <- rnorm(2*npts,rep(3:4,each=npts),sd=1) f <- factor(rep(1:2,each=npts)) boxplot(X~f) boxplot(X~f,notch=TRUE) I can

I am trying to draw a plot like Matlab does: The upper extreme whisker represents 95% of the data; The upper hinge represents 75% of the data; The median represents 50% of the data; The lower hinge represents 25% of the data; The lower extreme whisker represents 5% of the data. It looks like: --- 95% | | ------- 75% | | |-----| 50% | | | | -------

How are the lower/upper whiskers defined in the default version of boxplot {graphics}? I tried help(boxplot) and searching www.rseek.org, but I was unable to determine an absolute answer. I checked out the definition of boxplot according to Wikipedia (http://en.wikipedia.org/wiki/Box_plot), but it also had several approaches listed for how the whiskers could be determined, so I'm just

Sorry to repost this, but it looks like it's getting buried in r-help (originally posted October 5: my experience says that if it hasn't been answered by then it won't be). I wouldn't bother, but I'm worried that r-devel might be better, *and* a previous e-mail of mine on the subject in January also seemed to get buried. Synopsis: boxplot notches look weird when notches are

Hello R users wiser than I - I am trying to produce a boxplot with quantiles defined by the type 6 algorithm used mainly by Minitab and SPSS for a comparison study. I found how to compare the results in tabular form using the quantile(x, type = 6) function, but am stuck on how to show the results in a summary boxplot form. Would someone be able the help with this situation? Many thanks, Matt

For boxplot(), is it possible to pass in a parameter to change the default way that the 1st and 3rd quartiles are computed? (specifically, I'd like to use type 6 described in the quantile function). Also, what are the options for how outliers are computed, and how can one change them? Thank you [[alternative HTML version deleted]]

hi, i'm using /"boxplot()"/ to show some data: x <- c(0.99, 0.97, 0.91, 0.72, 1.00, 0.99, 1.02, 0.90, 0.91, 0.90, 1.02, 0.90, 1.35, 1.01, 0.92) boxplot(x) is it correct when i say: /"Boxes represent interquartile ranges (IQRs); bold horizontal lines, medians; whiskers, lowest and highest values still within 1.5 x IQR; open circles, outliers."? /thanks in advance

Dear r-help, Unfortunately I cannot find in the documentation what determines ranges of a 'box' in the box-and-whisker plot. It is said in "Simple R" (http://www.math.csi.cuny.edu/Statistics/R/simpleR) that they are 1st and 3rd Qus usually. I tried to add to boxplot lines with (quantile(x,probs=0.25)), but lines do not coincide with edges of boxes.

Hi, I have data that looks like this: ASA1 ASA2 C1_C2 C M 9.0225 S S 2.4315 M C 3.4894 M S 4.5282 C M 1.3183 C S 1.3735 S C 1.0488 S M 7.948 M C 4.5827 I need to plot Boxplots for a given ASA1 (either C,S, or M) with respect to C1_C2. However, instead of one boxplot I want to plot

A function I've been using for a while returned a surprising [to me, given the data] error recently: Error in plot.window(xlim, ylim, log, asp, ...) : Logarithmic axis must have positive limits After some digging I realized what was going on: x <- c(10460.97, 10808.67, 29499.98, 1, 35818.62, 48535.59, 1, 1, 42512.1, 1627.39, 1, 7571.06, 21479.69, 25, 1, 16143.85, 12736.96,

I've just started using R and am still a neophyte, but I found the following curious result. I'm using the current version of R (2.5.1 (2007-06-27) ). Why are the results for the third quartile different in the output from the summary and fivenum commands? For the following data set 457 514 530 530 538 560 687 745 745 778 786 790 792

Another naive stats question. I'm trying to better understand what boxplots are telling me. I think what I see is the median and the boundaries of the 1st and 3rd quartiles. The whiskers represent the range of the data unless there are points which are outside "range" (default: 1.5) times the distance from the median to that quartile. Is that right? I've read the

Hi All, could someone shed some light on what the difference between the estimated dispersion parameter that is supplied with the GLM function and the one that the 'gamma.dispersion( )' function in the MASS library gives? And is there consensus for which estimated value to use? It seems that the dispersion parameter that comes with the summary command for a GLM with a Gamma dist. is

Dear all, I am a total beginner in R, so sorry if this is the wrong place. I am using R 2.10.1 on a Mac (Mac OS 10.6.2). I have this small dataset : growth sugar 75 C 72 C 73 C 61 F 67 F 64 F 62 S 63 S I have no problem reading the table, or getting the summary, but if I try boxplot(growth~sugar, ylab="growth", xlab="sugar", data=Dataset), I have the following error :

Hi ,everyone. I want to draw a boxplot figure, main script shown as below. when I run the command "boxplot(bjavee~yearname)",it is wrong with the error information " model.frame.default(formula = bjavee ~ yearname) : variation 'bjavee' list is wrong". I want to add the yearname as the x-axis tick. And yearname is a array with the length 12,while bjavee is a 5*12