similar to: Boxplot with quartiles generated from different algorithms

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

Hello all- I would appreciate any guidance that can be provided. I am new to R and am using it exclusively in a statistics program I am undertaking that mainly references Minitab. My focus is on data modeling and further more multivariate data analysis as much of my work in involves chemical measurements from custom sensors using all sorts of transduction methods. I am looking for a

My box plot below is drawing its upper whisker all the way to the last point, instead of showing the point as an outlier. Am I misunderstanding, or is it a bug? Help(boxplot) states for the parameter ?range? that ?this determines how far the plot whiskers extend out from the box. If range is positive, the whiskers extend to the most extreme data point which is no more than range times the

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

Why am I getting a wrong result for quartiles? here is my code: > cbiomass = c(910, 1058, 929, 1103, 1056, 1022, 1255, 1121, 1111, 1192, > 1074, 1415) > summary(cbiomass) > IQR(cbiomass) The result R gives me is: For the summary > Min. 1st Qu. Median Mean 3rd Qu. Max. 910 1048 1088 1104 1139 1415 For IQR > 91.25 ********* The true Q1 is 1039

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

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

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.

Hello R-Users, The following questions are not R-technical, but more of general statistical nature. 1. NORMALITY I built a normal linear regression model and now I want to check for the residual normality assumption. If I check the distribution graphically and look at the descriptive characteristics (skewness and kurtosis are below 1), I would confirm that the residuals are normally

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

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

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

Dear R users, I have question concerning box plot and it's whiskers. As I understood from the description of the boxplot() function, if the range value is positive the plot whiskers extend out from the box to the most extreme data points defined by the values of the IQR times range (default 1.5). It suggests that the upper and lower plot whiskers should be more less the same length. What

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 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

Are you aware that there *already is* a function that does this? ?IQR (also your "function" iqr" is just a character string and would have to be parsed and evaluated to become a function. But this is a TERRIBLE way to do things in R as it completely circumvents R's central functional programming paradigm). Cheers, Bert Bert Gunter "The trouble with having an open mind

Hi all, I'm just getting started with R and I would appreciate some help. I'm having trouble creating a boxplot with whiskers at the 95th and 5th percentiles instead of at 1.5 * IQR. I have read the relevant documentation, and checked existing mails on this topic. I found a small modification that should work : https://stat.ethz.ch/pipermail/r-help/2001-November/016817.html and tried to

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 <-