search for: skewed

I'm able to recieve fax with pure SpanDSP 0.0.2 + Asterisk successfully but I have problems with some fax machine so I wanted to try using HylaFax to recieve Fax instead of SpanDSP hoping that it'll solve my problem. I'm trying to connect Asterisk with SpanDSP using iaxmodem. My system looks like this: ISDN <---> Asterisk <---> IAXModem <---> Hylafax Asterisk and

I'm able to recieve fax with pure SpanDSP 0.0.2 + Asterisk successfully but I have problems with some fax machine so I wanted to try using HylaFax to recieve Fax instead of SpanDSP hoping that it'll solve my problem. I'm trying to connect Asterisk with SpanDSP using iaxmodem. My system looks like this: ISDN <---> Asterisk <---> IAXModem <---> Hylafax Asterisk

Trying to do a net ads join, which has always worked fine in the past is now throwing the below errors when I try and rejoin the domain after a Windows server reboot. What am I doing wrong? :b! [2006/08/23 19:45:00, 0] libads/ldap.c:ads_add_machine_acct(1405) ads_add_machine_acct: Host account for mustang already exists - modifying old account [2006/08/23 19:45:00, 0]

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.

Hi all, I post it on both r-help and r-finance since I don't know where is most appropriate for this topic. Sorry if it bothers you. I did garch fitting on S&P500 monthly returns with garchFit from fSeries. I got same coefficients from all cond.dist except normal. I thought that is probabaly usual for the data. But when I play with it, I got another question. I plot skew normal with

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 functions the log-transformed data they return NaN > skewness( log( data ) ) #return...

> -----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)

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 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"

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 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'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]

Hi,   I use fCopulae package to draw different graphs of univariate and bivariate skew t.  But the plots titles overlap.  I tried using cex.main, font.main to adjust the size but they still overlaps.  Here is my code: par(mfrow = c(3, 1)) mu = 0 Omega = 1 alpha1 = 0 alpha2 = 1.5 alpha3 = 2 alpha4 = 0.5 Z1 = matrix(dmvst(x, 1, mu, Omega, alpha1, df = Inf), length(x)) Z2 = matrix(dmvst(x, 1, mu,

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/

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

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 transfer the skewed distribution data into normal distribution data, but failed. So what I can do is only find a calculation method to do with skewed...

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 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, 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 wi...