similar to: Box plot with 5th and 95th percentiles instead of 1.5 * IQR: problems implementing an existing solution...

Hello, I know this has been covered on here before, but as a complete novice, I need a little more guidance. I would like to produce boxplots with the whiskers extending to the 10 and 90th percentiles. I found this code: myboxplot.stats <- function (x, coef = NULL, do.conf = TRUE, do.out = TRUE) { nna <- !is.na(x) n <- sum(nna) stats <- quantile(x, c(.1,.25,.5,.75,.9), na.rm

Dear all, I constructed this function called my.boxplot.stats by replacing fivnum() with quantile() in function boxplot.stats(). So I can try different quantile methods in bwplot(). The problem is I couldn't pass different values to the "type" argument to my.boxplot.stats, which in turn is an argument in bwplot(). Now I just have to manually change the "type" value in

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

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

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,

Dear R users, Something is strange in summary and IQR. Suppose, I have a data set and I would like to find the Q1, Q2, Q3 and IQR. x<-c(2,4,11,12,13,15,31,31,37,47) > summary(x) Min. 1st Qu. Median Mean 3rd Qu. Max. 2.00 11.25 14.00 20.30 31.00 47.00 > IQR(x) [1] 19.75 However, I test the same data set in SAS "proc univariate", and SAS shows that

This report follows the post http://tolstoy.newcastle.edu.au/R/e6/devel/09/03/0760.html where it is shown that quantile() and IQR() do not work as documented. In fact they do not check for numeric input even if the documentation says = : ?quantile x numeric vectors whose sample quantiles are wanted. Missing values are ignored. ?IQR x a numeric vector. > quantile(factor(1:9)) 0%

Hi, I have two questions want to ask. 1. If I have a matrix like this, and I want to figure out the rows whose value in the 3rd column are less than 0.05. How can I do it with R. hsa-let-7a--MBTD1 0.528239197 2.41E-05 hsa-let-7a--APOBEC1 0.507869409 5.51E-05 hsa-let-7a--PAPOLA 0.470451884 0.000221774 hsa-let-7a--NF2 0.469280186 0.000231065 hsa-let-7a--SLC17A5

Hi guys! I have to calculate the 95% CI for the IQR. I have 100samples with continuous numbers using a compute-intensive method. 1)I can calculate the 95%CI and the IQR, but I don't know how to calculate the CI of the IQR, does anybody have any idea, which method will be appropriate? 2)I have a 2x2 table with gender vs blue eyes or not. How do I calculate the expected two-by-two contingency

Dear all, from the help page of quantile: "x ??? numeric vectors whose sample quantiles are wanted. Missing values are ignored." from the help page of IQR: "x ??? a numeric vector." as a matter of facts it seems that both quantile() and IQR() do not check for the presence of a numeric input. See the following: set.seed(11) x <- rbinom(n=11,size=2,prob=.5) x <-

The standard R boxplot appears to use quartiles to determine the height of the rectangles and a range parameter - RNG - (default=1.5 I think) that determines the length of the whiskers as <= RNG x Interquartile Range. Is it possible to instead define the range as extending to the 95th percentile? If so, how would this be done? nb, I'm plotting multiple boxplots on a single chart so a

Hi everyone, I think I have found a minor issue with the R function "boxplot.stats". But before I make such a rash comment, I'd like to check my facts by fixing what I think is the problem. However, why I try to do this, R does not behave as I expect. Can you tell me what I'm doing wrong? If I use the command: debug(boxplot.stats) I am allowed to step through the code as

Hello, I am trying to use the p.adjust function for multiple testing. here is what i have 9997 201674_s_at 0.327547396 9998 221013_s_at 0.834211067 9999 221685_s_at 0.185099475 I import them from excel have have the gene symbol as well as the pvalue here is the issue > pa<-p.adjust(pt,method="BH") Error in p[nna] : object is not

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

Dear R Helpers, I have run into a problem trying to perform a number of actions on a set of quantmod data objects through a loop and I am hoping that this is an easy problem for someone else as opposed to very difficult for me. The example task is to get the first three objects of the quarterly balance sheet for a number of companies from the getFinancials object and put them together into a

Dear List, I'd like to make boxplots of a large number of observations (+/- 20.000), which are distributed log-normal and right skewed. The problem is that with standard boxplots a too large number of observations are displayed as outliers. I also tried to display the log of the observations, but even then there are to may outliers to my taste. So I'd like to change the standard IQR box

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Dear list members, Can anyone tell me how the notches in boxplot(Y~X,notch=T) are calculated? What do these notches represent exactly? I?d suppose they are Conficence Intervals for the median, but I?ve also been told they might show Least Significant Difference (LSD) equivalents. I would very much appreciate any help from you. Best regards Chris.

Is there a way to modify the choice of notch size [1] in R's boxplot routine from outlining a 5% significance region, to say 1% or lower? Thanks, Alex [1] McGill, Tukey, and Larsen. "Variations of Box Plots", The American Statistician, Vol. 32, No. 1, 12-16.

I noticed that boxplot computes a 95% CI for the median by using median +/- 1.58*IQR./sqrt(n) Where does the 1.58 constant come from? -- Rick White Statistical Consultant U.B.C. Vancouver B.C. Canada rick at stat.ubc.ca -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send