similar to: Mean, median and other moments

Dear R helpers,   I am working on the scenario analysis pertaining to various interest rates. In this connection I need to form the various combinations as under :   Suppose I have two sets A = (a, b, c) and B = (x,y,z)   Then I can easily form the cominations as (ax, ay, az, bx, by, bz, cx, cy, cz)   However, if I have say 5 variables, then total no of possible combinations will be 3^5 = 243.

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, 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 helpers   After executing the R code, where the last few lines of the code are something like given below.     ## Part of my R code     n = 20   ........ .........   final_output = data.frame(Numbers = numbers, ABC = data1, XYZ = data2, PQR = data3)           write.csv(data.frame(Scenario = paste("Sc_", 1:n, sep = ""), final_output'), 'result.csv', row.names

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

Dear R helpers   Suppose e.g. I have a csv file having three variables defined and each of these variables have data items of say 40, 50, 45 length. When I open this csv file in 'R', I get 10 trailing 'NA's under first column and 5 'NA' s in case of 3rd column.   How do I get rid of these NA's s.t. when I read the first column, there should be only 40 data items, 2nd

Dear 'R' friends I have a sort of stupid question to ask. I have a matrix say of the order 4 X 3 as 83    98    90 21    83    84 70    39    56 65    29    38 Is there any command in R which will reverse the order i.e. I need to have same 4 X 3 matrix but as given below 65    29    38 70    39    56 21    83    84 83    98    90 i.e. the last row becomes first row, second - last row

  Dear R helpers   Suppose I have two sets of ranges (interest rates) as   Range 1 : (7 – 7.50, 7.50 – 8.50, 8.50 – 10.00) with respective probabilities 0.42, 0.22 and 0.36.     Range II : (11-12, 12-14, 14-21) with respective probabilities 0.14, 0.56 and 0.30 respectively.     My problem is to form the combinations of these ranges in a decreasing order of joint probabilities. It is assumed that

Dear R helpers If for a given data, I need to calculate Mean, Standard Deviation, Mode, Median, Skewness, Kurtosis, is there any package in R, which will calculate these moments? Individually I can calculate these, but if there is any function which will calculate these at a stretch, please let me know. Maithili The INTERNET now has a personality. YOURS! See your Yahoo! Homepage.

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 all, I'm wondering whether it is possible to construct a confidence interval using only the mean, variance, skewness and kurtosis, i.e. without any of the population? If anyone could help with this it'd be much appreciated (even if just a confirmation of it being impossible!). Thanks. -- View this message in context:

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. --

Hello, I know that Mclust does the fitting on its own but I am trying to implement an optimization with the aim to generate a the mixture gaussian with the combine moments as closed as possible to the moment of my return distribution. The objective is to Min Abs((Mean Ret - MeanFit)/Mean Fit) + Abs((Std Ret -Stdev Fit)/Stdev) + Abs((Sk Ret-Sk fit)/Sk Fit) + Abs((Kurt Ret- Kurt Fit)) Taking

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

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-Users, The following questions are not R-technical, but more of general statistical nature. 1. NORMALITY I built a normal linear regression model and now I want to check for the residual normality assumption. If I check the distribution graphically and look at the descriptive characteristics (skewness and kurtosis are below 1), I would confirm that the residuals are normally

The code I'm using is shown below. I would like to have a larger median at the top of the plot so that I can show the entirity of "title_text". Several times I tried messing with "par(mar", but that seemed to make matters worse. By any chance can anyone provide any insight as to the best way to increase the top/upper/northern margin, so the entirity of the title is

I have an external data (.txt) for annual peak flood. The first column is the year, second column is the observation date, and the last is the observed discharge. My task is to calculate the mean, skewness and kurtosis of the said data. I was advised to use read.table() to read the entire data. Please help me on how to perform the required computation. I am obviously a new user of this statistical

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 Bill, Thanks for prompt reply and letting me know a way around it. I have more than 1200 observations and not all the values are the same. However, my data points are quite similar, for example, 0.079275, 0.078867, 0.070716 in millions and etc. I have run the data without converting it to millions and I still get the same error message. As I have kurtosis value, it should be fine for the