similar to: moments, skewness, kurtosis

It appears that the maximum number of connections available in R is about 48. Can anyone tell me how to bump this number up? I've been perusing the source, but any info would speed things up. Is there a reason that it was set to such a low number? Thanks for any help. -Frank

The Intel compiled version also fails the below test: > ###------------ Very big and very small > umach <- unlist(.Machine)[paste("double.x", c("min","max"), sep='')] > xmin <- umach[1] > xmax <- umach[2] > tx <- unique(outer(-1:1,c(.1,1e-3,1e-7)))# 7 values (out of 9) > tx <- unique(sort(c(outer(umach,1+tx))))# 11 values

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]

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")

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'm running R-2.0.0 (yesterday's snapshot)in its own directory, and everything works great, except: > .Random.seed Error: Object ".Random.seed" not found Does 2.0.0 not use .Random.seed for saving, etc, like it says in the help page? Thanks for any help. -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 #

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 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

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

Is there a way to tell how much memory the computer running R has? -Frank -----Original Message----- From: David Smith [mailto:dsmith at insightful.com] Sent: Monday, February 09, 2004 1:32 PM To: Ross Boylan Cc: r-help Subject: RE: [R] R does in memory analysis only? Ross Boylan writes: > R works only on problems that fit into (real or virtual) memory. > ... does S-Plus have the same

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 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 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 <-

Hi again, thanks a lot for the quick answer. I just forgot the comma, always these stupid mistakes... Anyways, as I said before, I have two data.frames containing about 1000 rows and I would like to avoid looping through all of them... In each data.frame are coordinates (x,y,z), so every row is giving the information on one single point. I would like to calculate the distance from each point

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

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.

Hi all, I'm wondering whether it is possible to construct a confidence interval using only the mean, variance, skewness and kurtosis, i.e. without any of the population? If anyone could help with this it'd be much appreciated (even if just a confirmation of it being impossible!). Thanks. -- View this message in context:

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