similar to: Labelling boxplot with fivenumber summary

Dear list, How can I add the mean and standard deviation to each of the boxplots using the example provided in the boxplot function? boxplot(len ~ dose, data = ToothGrowth, boxwex = 0.25, at = 1:3 - 0.2, subset = supp == "VC", col = "yellow", main = "Guinea Pigs' Tooth Growth", xlab = "Vitamin C dose mg",

Hello, I have a couple more questions about boxplots. In the books I've read on statistics (I'm not a statistics expert but just a poor engineer trying to help his wife with the analysis of the data she gathered for her medieval history PhD), I seen two kinds of box plots: one using quartiles to determine the height of the rectangles and whiskers which is what the standard R boxplot does,

On OpenBSD we still use the "classic" SunOS 4 shared library versioning scheme where the major and minor number are part of the library name (and recorded in DT_NEEDED entries). For example the shared libc on the OpenBSD-current is named libc.so.89.2. With this scheme, linker has to pick the pick the library with the highest major and minor (within the highest major version); the

Hi Mark, If we have to do this, or LLD doesn't work on OpenBSD, I think we need to do this. But can I ask one question? I wonder why OpenBSD systems don't have symbolic links unlike the other Unix-like systems in the first place. On Mon, Dec 19, 2016 at 2:27 PM, Mark Kettenis via llvm-dev < llvm-dev at lists.llvm.org> wrote: > On OpenBSD we still use the "classic"

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 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

Hi all, I am a beginner trying to use R to work with large amounts of oceanographic data, and I find that computations can be VERY slow. In particular, computational speed seems to depend strongly on the number and size of the objects that are loaded (when R starts up). The same computations are significantly faster when all but the essential objects are removed. I am running R on a machine

I really like the ease of use with the boxplot command in R. I would rather have a boxplot that shows the average value and the standard deviation then the median value and the quartiles. Is there a way to do this? Chad Junkermeier, Graduate Student Dept. of Physics West Virginia University PO Box 6315 210 Hodges Hall Morgantown WV 26506-6315 phone: (304) 293-3442 ext. 1430 fax: (304)

When data contains both factor and numeric variables, how to get quartiles for all numeric variables? n <- 100 x1 <- runif(n) x2 <- runif(n) x3 <- x1 + x2 + runif(n)/10 x4 <- x1 + x2 + x3 + runif(n)/10 x5 <- factor(sample(c('a','b','c'),n,replace=TRUE)) x6 <- factor(1*(x5=='a' | x5=='c')) data1 <- cbind(x1,x2,x3,x4,x5,x6) data

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'm trying to create a qqplot with reference to a Weibull distribution including a reference line. This is my current code: lights.data <- scan("lights.dat") #Generate Weibull quantiles prob.grid <- ppoints(length(lights.data)) prob.quant <- qweibull(prob.grid , 1.5,4) #Draw QQ plot qqplot(prob.quant,lights.data) #add red reference line qqline(lights.data,col = 2)

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

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

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

Hello, I'm currently using a simple plot to visualize some mean values. I'm having ~200 datapoints on the x-axis, each has 10 records. I'm currently plotting only the mean value of each of the datapoints. What I need is a way to visualize the quartiles/error/whatever of these points. I thought about boxplots, but I have to many points on the xaxis - it would be impossible to see

I think qqline does not do exactly what it is advertised to do ("`qqline' adds a line to a normal quantile-quantile plot which passes through the first and third quartiles."). Consider the graph: tmp <- qnorm(ppoints(10)) qqnorm(tmp) qqline(tmp) The line (which I expected go through all the points), has a slightly shallower slope than does the points plotted by qqnorm. I think

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

I'm trying to devide x into tertiles, but ends up with integer limits even x holds one decimal. The analysis is extremely sensitive to the limits and I like to keep them right. How can that be done? quartiles <- quantcut( x[x >= 0], q=seq(0,1, by=(1/3)) > table(quartiles) quartiles [180,344] (344,448] (448,644] 16467 16476 16452 [[alternative HTML version deleted]]

Dear all, The incredibly useful Hmisc package provides a method to generate summary tables that can be typeset in latex. The Alzola and Harrell book "An introduction to S and the Hmisc and Design libraries" provides an example that generates mean and quartiles for continuous variables, and numbers and percentages for count variables: summary() with method = 'reverse'. I

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