similar to: kurtosis

Dear All, I have made a Q-Qplot (qqnorm) and want to change the scale of the y-axis from -4 to 25. How can I do that? Many thanks Samira

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]

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. --

Dear friends, I try to get started with R and can’t estimate kurtosis of a random sample by using one command. I have installed R 1.6.2. Please help. Max --------------------------------- ur needs [[alternate HTML version deleted]]

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")

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)))

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 #

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

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 :

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

Hello everybody, this is a bit off topic, so maybe you can just reply to me personally... What is the typical notation for 'skewness', 'kurtosis' and maybe 'excess kurtosis'? Thank you, Martina -------------------------------------------------------------------------- Department of Statistics Office Phone: (614) 292-1567 1958 Neil Avenue, 304E Cockins Hall

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

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 ==========================

Hello everybody, I'm studying descriptive statistics with R and I want to know how to calculate the asymmetry and kurtosis coefficients of a sample using R. I'll appreciate some help. Thanx, Talita Perciano Costa Leite Graduanda em Ci?ncia da Computa??o Universidade Federal de Alagoas - UFAL Departamento de Tecnologia da Informa??o - TCI Constru??o de Conhecimento por Agrupamento de

Need help! I know how to get the mean and kurtosis for a single variable but I am reading in an excel file that has several columns representing variables. I need a way to find descriptive statistics across ALL the variables? [[alternative HTML version deleted]]

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 <-

[forwarded to R-devel, where this really belongs] >>>>> "Samira" == Samira M Ismail <samira@ime.unicamp.br> writes: (to me in a private mail) Samira> We have been trying to install R.0.63 for Unix (SunOS 4.1.4 - Samira> SPARC 20) and we detected the following problem which makes Samira> impossible to run R. We have got the following message:

[forwarded to R-devel, where this really belongs] >>>>> "Samira" == Samira M Ismail <samira@ime.unicamp.br> writes: (to me in a private mail) Samira> We have been trying to install R.0.63 for Unix (SunOS 4.1.4 - Samira> SPARC 20) and we detected the following problem which makes Samira> impossible to run R. We have got the following message:

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

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