similar to: function centralm - does it exist

Dear R colleagues, This seems pretty straight forward but I have been banging my head on this for some time and can't seem to find a solution suppose I have something like a1<-1; a2<-2; a3<-3; a4<-4; b1<-3; b2<-4 I would like to quickly assign to objects with a certain pattern, e.g., those in ls(pattern="a") a specific value, e.g., "99", without

Hi, When I run this code a part of my x-axis and y-axis changes color. Can somebody tell me what is wrong? Also, is there a way to control the color of the average lines? monthplot(AirPassengers+500, ylim=c(min(AirPassengers), max(AirPassengers+500)), ylab="") par(new=T) monthplot(AirPassengers, col="blue", ylim=c(min(AirPassengers), max(AirPassengers+500)),

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 colleagues, Can anyone of you tell me how to write a "nabla" symbol in an R graph? Thanks in advance, Nuno ______________________________________________ Centro de Oceanografia - IO-FCUL, Portugal Center for Quantitative Fisheries Ecology - ODU, USA [[alternative HTML version deleted]]

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

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 #

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 colleagues, ?arima? returns directly the 1-step ahead errors but I am interested in obtaining other h-step ahead errors for several ARIMA models I have fitted. Is there any way I can obtain this with R? Any help would be appreciated. Sincerely, Nuno Prista _________________________ CO - FCUL, Lisboa, Portugal CQFE - ODU, Norfolk, USA

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 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?

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

> 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 <-

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, 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

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-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 am far from experienced in both R and time series hence the question. The code for spec.pgram() seems to involve a circularity of the kernel (see below) yielding new power estimates to all frequencies computed by FFT. " if (!is.null(kernel)) { for (i in 1:ncol(x)) for (j in 1:ncol(x)) pgram[, i, j] <- kernapply(pgram[, i, j], kernel, circular = TRUE)

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)))