similar to: Confidence Interval from Moments?

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, 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]

Hi! Suppose I have a dataset as follows pd = c(10,7,10,11,7,11,7,6,8,3,12,7,7,10,10) I wish to calculate the mean, standard deviation, median, skewness and kurtosis i.e. regular standard statistical measures. average = mean(pd) stdev    = sd(pd) median = median(pd) skew    = skewness(pd) kurt     =  kurtosis(pd) Q. No (1) How do I get these at a stretch using some R package? I came across

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

Dear R helpers If for a given data, I need to calculate Mean, Standard Deviation, Mode, Median, Skewness, Kurtosis, is there any package in R, which will calculate these moments? Individually I can calculate these, but if there is any function which will calculate these at a stretch, please let me know. Maithili The INTERNET now has a personality. YOURS! See your Yahoo! Homepage.

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 #

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. --

hello R, This is about simulation in psychomtrics in IRT in R. I want to simulate b parameters(item difficulty) with moments of fixed values of mean, st.d, skewness and kurtosis. Is there any specific IRT package in R with those functions to control those moments? I have seen other programs that can control mean and st.d but not skewness and kurtosis. Thank you, helen L [[alternative HTML

Greetings Everybody: I generated a 1.2MB dta file based on the general social survey with Stata8 for linux. The file can be re-opened with Stata, but when I bring it into R, it says all the values are missing for most of the variables. This dataset is called "morgen.dta" and I dropped a copy online in case you are interested http://www.ku.edu/~pauljohn/R/morgen.dta looks like this

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

Folks, I'm a bit puzzled by the fact that if I generate 100,000 standard normal variates using rnorm() and perform the Jarque-Bera on the resulting vector, I get p-values that vary drastically from run to run. Is this expected? Surely the p-val should be close to 1 for each test? Are 100,000 variates sufficient for this test? Or is it that rnorm() is not a robust random number generator?

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

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)))

> exampledata <- rnorm(10000) > summary(exampledata) Min. 1st Qu. Median Mean 3rd Qu. Max. -4.030000 -0.666200 -0.023390 -0.009384 0.664700 4.092000 > desc <- function(mydata) { + require(e1071) + quantls <- quantile(x=mydata, probs=seq(from=0, to=1, by=0.25)) + themean <- mean(mydata) + thesd <- sd(mydata) + kurt <-

Hi all. This is my first post in this forum. Finally I find a forum in the web about R, although is not in my language. Now I'm working with Bootstrap CI. I'd like to know how I can calculate a Bootstrap CI for any statistic, in particular, for Kurtosis Coeficient. I have done the following code lines: > library(boot) > x=rnorm(20) > kurtosis=function(x)

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 <-

Hello, I know that Mclust does the fitting on its own but I am trying to implement an optimization with the aim to generate a the mixture gaussian with the combine moments as closed as possible to the moment of my return distribution. The objective is to Min Abs((Mean Ret - MeanFit)/Mean Fit) + Abs((Std Ret -Stdev Fit)/Stdev) + Abs((Sk Ret-Sk fit)/Sk Fit) + Abs((Kurt Ret- Kurt Fit)) Taking

Hello please note that the code at https://stat.ethz.ch/pipermail/r-help/2006-August/110892.html to compute indices of skewness and kurtosis for the skew-t distribution is not correct. It has been kindly pointed out that i made some error in this code, which was a bit too quickly copied from the paper. The 'sn' package already contains a facility for computing the cumulants, namely

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

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 :