similar to: Comparing skewness of two distributions

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

Hello, I'd appreciate any ideas on how to compare the skewness of two samples. In my case, one sample is likely to be roughly normal and the other one skewed. I could run two D'Agostino tests, but then I'll have to correct for the family-wise error. What if both samples are skewed? If there are no general tests (or they can't exist), I'd like to know. Thanks, Skirmantas

I have summary statistics from many sets (10,000's) of near-normal continuous data. From previously generated QQplots of these data I can visually see that most of them are normal with a few which are not normal. I have the raw data for a few (700) of these sets. I have applied several tests of normality, skew, and kurtosis to these sets to see which test might yield a parameter which

Hi, I need to create a plot (type = "h") and then overlay a skewed-normal curve on this distribution, but I'm not finding a procedure to accomplish this. I want to use the plot function here in order to control the bin distributions. I have explored the sn library and found the dsn function. dsn uses known location, scaling and shape parameters associated with a given input

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]

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 also try to fit a skewed distribution (like skewed student t) to data points. Do you have an idee howto do this??? thank you fabrice dusonchet *********************************************************************************** This email and any files transmitted with it are confidentia...{{dropped}}

Hi all: I have a question on sample size calculation of 2 groups of data. If 2 groups of data are all normal distribution, then I can use the function "n.indep.t.test.eq" from samplesize package.But if 2 groups of data are all skewed distribution, but not normal distribution,how can I calculate the sample size then? I've tried many transformation (e.g. log arcsin…) in order to

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

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. --

On May 24, 2017 1:08:09 PM GMT+02:00, Charles Lepple <clepple at gmail.com> wrote: >On May 24, 2017, at 5:11 AM, Arnaud Quette <arnaud.quette at gmail.com> >wrote: >> >> Hi all, >> >> here is another one, related to ATS (automatic transfer switch) this >time. >> >> in order to track "dephasing" between input sources (1 and 2),

> -----Original Message----- > From: r-help-bounces at stat.math.ethz.ch > [mailto:r-help-bounces at stat.math.ethz.ch]On Behalf Of Caio Lucidius > Naberezny Azevedo > Sent: 01 September 2005 12:09 > To: Help mailing list - R > Subject: [R] Multivariate Skew Normal distribution > > > Hi all, > > Could anyone tell me if there is any package (or function)

[This email is either empty or too large to be displayed at this time]

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 :

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, Is there a function in R that I can use to fit the data with skew t distribution? Speaking in detail, I first used the kernel density estimation to fit my data, then I drew the skew t using my specified location, scale, shape, and df to make it close to the kernel density. Now I want to get the parameter estimations of the skew t which give me the closet density to the kernel density.

If I create an aggregation like this: aggregate(lastYear[,8:10],list(Stadium=lastYear$STAD),mean) I'll get a new data frame, which I can order if I assign it like this: newFrame <- aggregate(lastYear[,8:10],list(Stadium=lastYear$STAD),mean) newFrame[order(newFrame$TEMP),] But.. if I just want to do this in one statement, what can I do? If this was perl, I'd be using something like

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

Hello everybody, I need your help about the package SN and the skew student distribution. Il will be very grateful if I have the solution. I construct a stochastic model with a white noise not gaussian but following a skew student distribution. I fit the noise on monthly data to obtain the four parameters. The question is : how to annualize the parameters to use my model for simulate daily data

I'm trying to identify patterns among various "paths" like the following: http://i.imgur.com/bQPI3.png If I plot these, I can observe intuitively two different patterns: a front loaded (1 and 3) and a backloaded (2,4) progress path: http://i.imgur.com/L5qwZ.png I have thousands of observations like the above table, and I want to use R to identify clusters of these paths. I