similar to: Coefficient of skewness

Hi all, Is there an equivalent to the general * operator in R, where * can represent any character? I have a dataset, one column being date, date ranging between April-September, 97-06. I would like to be able to acquire the data for a specific month, say September, so that I can take average temperatures for the month, etc. I thought I would be able to do something like data.s1 <-

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. --

Hi, Appologies for the simple nature of this question, I am unable to find the answer in manuals (EG and introduciton to R). I have written a function in a text editor and saved it with an .R extension. It is saved in my working directory. How can I run it, do I need to use source? If so, how do I supply the arguments to the function? Or does it need to be saved in a particular directory?

Hello everyone, Does anyone know what would cause the skewness() function (from e1071), as well as skew() from psych, to return a value of NaN? I have a vector of positively-skewed data (https://docs.google.com/file/d/0B6-m45Jvl3ZmYzlHRVRHRURzbVk/edit?usp=sharing) which these functions return a value for like normal: > skewness( data ) # returns 1.400405 but when I instead give those

Dear all, I am having difficulties using the seemingly-simple function lag. I have a dataframe with several weather variables (maxitemp, windspeed, rainfall etc), and the response variable (admissions). The dataset is fairly large (1530 observations). I simply want to model the response against a lag of a couple of the explanatory variables, say maxitemp and rainfall. I would like to look at

Dear all, I appologise for cluttering up the list with such a basic question, however I have been unable to find the answer I want (possibly through my poor usage of the R help system). As I am visually impaired and using assistive technology, I think I would prefer to use R from the terminal mode, i.e. by running rterm.exe found in the bin directory. I have managed to set my working

Hi, is there a test for the H0 skewness=0 (or with skewness as test statistic and normality as H0) implemented in R? Thank you, Christian *********************************************************************** Christian Hennig Fachbereich Mathematik-SPST/ZMS, Universitaet Hamburg hennig at math.uni-hamburg.de, http://www.math.uni-hamburg.de/home/hennig/

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)))

Hi all, Thanks for the help with my previous post. I have just two more questions for the minute. I think I said in a previous post that I like to use the terminal, i.e. run rterm.exe. On exiting the terminal, I am asked if I want to save the workspace. If I hit y (yes), the workspace is just saved as .rdata in my working directory, does anyone know how I can name it directly from the

Hi, I have eight vectors that I would like to orthogonalize preferably using R. The vectors are of considerable length, however due to their nature I know they satisfy the conditions needed to apply the Gram-Schmidt algorithm. Before I embark on some R coding, I wanted to check that there is no facility / function already around that computes the orthogonalized set of vectors? I have performed

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

Hi all, I am very confused with class. I am looking at some weather data which I want to use as explanatory variables in an lm. R has treated these variables as factors (i.e. with different levels), whereas I want them treated as discretely measured continuous variables. So I need to reassign the class of these variables, right? Indeed, doing class(southwest$pressure) (pressure being air

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 think it is simply, but I cannot find the method to figure out "skewness". Thanks! [[alternative HTML version deleted]]

Hi, Is there any quick and easy way to rename a number of objects, without having to rename each one individually and then remove the old one? And if so, is there anything I can do to adjust the associated comments accordingly? Thanks for any help, Robin Williams Met Office summer intern - Health Forecasting robin.williams@metoffice.gov.uk [[alternative HTML version deleted]]

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 #

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 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 <-

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

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]