similar to: Looking for a comprehensive descriptive statistics package

> 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 <-

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 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)))

Hello there, I’m computing the total value of an order from the price of the order items using a “for” loop and the “ifelse” function. I do this on a large dataframe (close to 1m lines). The computation of this function is painfully slow: in 1min only about 90 rows are calculated. The computation time taken for a given number of rows increases with the size of the dataset, see the example with

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 everybody I am a recent convert from SAS so please excuse me if this is all very obvious: I want to use the runs test {runs.test() in package tseries} to test the randomness of a certain variable in a survey for each interviewer. I tried to us the by() statement but it doesn't seem to work with runs.test() as the function. Here is what I have: Consider a data frame with two variables and

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

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

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 dataframe with several different treatment variables, and would like to calculate the mean and standard deviation of the replicates for each day and treatment variable. It seems like it should be easy, but I've only managed to do it for one treatment at a time using subset and tapply. Here is an example dataset: > `exampledata` <- structure(list(day = c(1L, 1L, 1L, 1L, 1L,

Dear All, I have these data: exampledata <- c("This is one item", "This is Another One", "And so is This") I would like to find each occurence of a blank space followed by a Capital Letter and replace it by a blank space, a left curly brace, the respective Capital Letter, and then a right curly brace. I thought the following will do: gsub(pattern = "

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

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]

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 all, I improved a function drawing horizontal histograms (code below) which uses barplot and works fine. It does,however, need to assign a function to the global environment to later find the actual location on the vertical axis, and not the number of bars used by barplot. Hopefully, running the examples below will illustrate that. As said, it works perfectly fine and does exactly what I

Dear all, Being a newbie to R, I've trawled through many old posts on this list looking for a solution to my problem, but unfortunately couldn't quite figure it out myself. I'd be very grateful if someone here on this list could perhaps help me out. I have a lattice plot with several panels and would like to add some text next to the y-axis on the right hand side of each row of

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?

If I have data that I feed into shapio.test and jarque.bera.test yet they seem to disagree. What do I use for a decision? For my data set I have p.value of 0.05496421 returned from the shapiro.test and 0.882027 returned from the jarque.bera.test. I have included the data set below. Thank you. Kevin "Category","Period","Residual" "CHILD HATS, WIGS &