similar to: Testing two independent samples for null of same skewness and kurtosis?

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]

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

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. --

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

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

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

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 :

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)))

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")

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

Hello all, I need to simulate rating scale from skewed and kurtosis. I would like to simulate a set of (discrete) data. ( n=1000, Item = 50 and 5 choice (1-5) ) Regards, Vichr -- View this message in context: http://r.789695.n4.nabble.com/How-to-simulate-rating-scale-from-skewed-and-kurtosis-tp4363805p4363805.html Sent from the R help mailing list archive at Nabble.com.

HI Philippos, Try this: dat1<- read.csv("Validation_data_set3.csv",sep=",",stringsAsFactors=FALSE) #converted to csv str(dat1) #'data.frame':??? 12573 obs. of? 17 variables: # $ Removed.AGC????????????????????????????? : num? 65.67 46.17 41.26 14.09 5.38 ... # $ Removed.SST????????????????????????????? : chr? "" "46.1658" "41.2566"

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