similar to: notation for skewness and kurtosis

Hi all, I encountered a difference between versions 1.6.1 and 1.7.0 when using levelplot with an option 'at'. Here are the specs of the two platforms used: > R.version _ platform sparc-sun-solaris2.8 arch sparc os solaris2.8 system sparc, solaris2.8 status major 1 minor 6.1 year 2002 month 11 day 01 language R > R.version _ platform

Hi all, I would like to draw one picture which would show two different types of boxplots using the same axes (kind of on top of each other). However, I would like to plot each boxplot using a different color or different shading inside the box, so they could be better distinquished from each other... Could you help me? Here is an example of the plot I have so far. I was only able to change the

Dear R-helpers, I am trying to install Rstreams library on the following platform: ---------------------------- platform alphaev67-dec-osf5.0 arch alphaev67 os osf5.0 system alphaev67, osf5.0 status major 1 minor 2.3 year 2001 month 04 day 26 language R --------------------------- Unfortunately, I get these error messages: --------------------------- > [557] R

without having to check the vector element by element? Thanks a lot! Jason ===== Jason G. Liao, Ph.D. Division of Biometrics University of Medicine and Dentistry of New Jersey 335 George Street, Suite 2200 New Brunswick, NJ 08903-2688 phone (732) 235-8611, fax (732) 235-9777 http://www.geocities.com/jg_liao

The sapply function is refusing to return a result for what seem to me to be mysterious reasons. Here is a toy example: set.seed(111) X <- list(x=runif(20),y=runif(20)) rvec <- seq(0.01,0.15,length=42) foo <- function(x,X,cc) { mean((X$x)^x + (X$y)^cc) } bar <- function(x,a,b){a+b*x} try.b <- sapply(rvec,bar,a=1,b=2) # This runs without a problem and

Dear R users, I hope to explain the concepts of skewness and kurtosis by generating series of distributions with same skewness and different kurtosis or with same kurtosis and different skewness, but it seems that i cannot find the right functions. I have searched the mailing list, but no answers were found. Is it possible to do that in R? Which function could be used? Thanks a lot. --

Hi, I'm a newcomer to R, having previously used SPSS. One problem I have run into is computing kurtosis. A test dataset is here: http://www.whinlatter.ukfsn.org/2401.dat > library(moments) > data <- read.table("2401.dat", header=T) > attach(data) > loglen <- log10(Length) With SPSS, I get Skewness -0.320 Kurtosis -1.138 With R: > skewness(loglen) [1]

I checked the help and the mailing list archives, but I can find no mention of a routine that calculates higher moments like skewness and kurtosis. Of course, these are easy enough to write myself, but I was thinking that they MUST be in here. Am I wrong? Thanks. -Frank

I have a question about the D'Agostino skewness test and the Anscombe-Glynn kurtosis test. agostino.test(x, alternative = c("two.sided", "less", "greater")) anscombe.test(x, alternative = c("two.sided", "less", "greater")) The option "alternative" in those two functions seems to be the null hypothesis. In the output, the

Hi, in which R-package I could find skewness and kurtosis measures for a distribution? I built some functions: gamma1<-function(x) { m=mean(x) n=length(x) s=sqrt(var(x)) m3=sum((x-m)^3)/n g1=m3/(s^3) return(g1) } skewness<-function(x) { m=mean(x) me=median(x) s=sqrt(var(x)) sk=(m-me)/s return(sk) } bowley<-function(x) { q<-as.vector(quantile(x,prob=c(.25,.50,.75)))

Is there some reasonable way to generate random data from a distribution that has some degree of skewness and/or kurtosis, but would otherwise be normal? thanks, -------------- next part -------------- A non-text attachment was scrubbed... Name: greiff.vcf Type: text/x-vcard Size: 398 bytes Desc: Card for Warren R. Greiff Url :

Dear all, Right now, I'm doing research about outlier in statistical data (univariate and multivariate) and I want to simulate its behavior. My problem is : How to generate random data from distribution with zero skewness and some kurtosis values in R ? A. Kudus ===================== Dept. of Statistics Bandung Islamic University I n d o n e s i a ==========================

Dear R-Users and Developpers, Currently R does not include functions to compute the skewness and kurtosis. I programmed it myself in the following way, but probably *real* programmers/statisticians can do that better: mykurtosis <- function(x) { m4 <- mean((x-mean(x))^4) kurt <- m4/(sd(x)^4)-3 kurt } myskewness <- function(x) { m3 <- mean((x-mean(x))^3) skew <-

I wonder whether the functions for skewness and kurtosis in the e1071 package are based on correct formulas. The functions in the package e1071 are: # -------------------------------------------- skewness <- function (x, na.rm = FALSE) { if (na.rm) x <- x[!is.na(x)] sum((x - mean(x))^3)/(length(x) * sd(x)^3) } # -------------------------------------------- and #

Thanks to all who response my problem. Here are my summary : 1. from Dirk Eddelbuettel <edd at debian.org> We could try a mixture of normals -- ie flip a coin (use a uniform with some cutoff c where 0 < c < 1 ) to choose between N(0, sigma_1) and N(0, sigma_2). 2. from Michaell Taylor <michaell.taylor at reis.com> We could use the gld library to specify the lambdas of

Hello I wonder whether there is an r tool or package available for testing for the null of same skewness or kurtosis of two independent samples. It semes that nsRFA package uses L-moments for soothing similar but I could not get how to use the package for the above test. Any pointers, help, example and etc. will be most welcome. Many thanks Ed [[alternative HTML

I'm sorry, but my previous message, as often happens, some brackets were wrong: Here are the correct formulas: sd = 1/n * sum((x-mean(x))^2) # (1) sd = 1/(n-1) * sum((x-mean(x))^2) # (2) This also occured in the last paragraph. Dirk ************************************************* Dr. Dirk Enzmann Institute of Criminal Sciences Dept. of Criminology Edmund-Siemers-Allee 1 D-20146

Hello, I'm quite new to R but tried to learn as much as possible in the last few months. My problem is that I would like to estimate the model of Leon et al. (2005). I have shortly summarised the most important equations in the following pdf file: http://hannes.fedorapeople.org/leon2005.pdf My main question is now how could I introduce these two additional terms into the Likelihood

Dear R-users, I am comparing differences in variance, skew, and kurtosis between two groups. For variance the comparison is easy: just var.test(group1, group2) I am using agostino.test() for skew, and anscombe.test() for kurtosis. However, I can't find an equivalent of the F.test or Mood.test for comparing kurtosis or skewness between two samples. Would the test just be a 1 df test on

Hello I do not know very much about statistics (and English language too :-( ), then I come in search of a clarification (explanation): I found two distinct results on KURTOSIS and I do not know which of them is the correct one. Any aid will be welcome! klebyn ################ CODE rnorm(1000) -> x library(moments) kurtosis(x) skewness(x) detach("package:moments")