similar to: multivariate normality test

Dear all, I want to save the p-value from mshapiro.test(mvnormtest). But mshapiro.test(mvnormtest) gives a list with class "htest" containing statistic, p.value, method and data.name as a whole. How do I save only p-value from the result? Thanks a lot!

Dear all, I got this error message > library(dprep) > mardia(Savg) Error in cov(data) : 'x' is empty But with the same data, I got > library(mvnormtest) > mshapiro.test(Savg) Shapiro-Wilk normality test data: Z W = 0.9411, p-value = 0.6739 What does the error message "Error in cov(data) : 'x' is empty" mean? Thanks a lot! Jiao

Hi, I have a question about the p-value of mshapiro test. I simulated data from bivariate normal 1000 times and used mshapiro test to see how many times the test would reject the null hypothesis when the p-value is 0.05. The answer should be around 50 since the p-value is 0.05. But I got a much higher value. Here is the R code I used and the result.

Hi, I'm working with a numeric matrix with 55columns and 581012 rows and I'm having problems in allocation of memory using some of the functions in R: for example lda, rda (library MASS), princomp(package mva) and mvnorm.etest (energy package). I've read tips to use less memory in help(read.table) and managed to use some of this functions, but haven't been able to work with

R Users, We would like to announce that Version 1.0.1 of the energy package is now available on CRAN. The energy package introduces a new class of statistical tests based on the concept of Newton's potential energy. Included in the package are * mvnorm.etest (test) and mvnorm.e (statistic) A rotation invariant multivariate goodness-of-fit test, implemented for testing

R Users, We would like to announce that Version 1.0.1 of the energy package is now available on CRAN. The energy package introduces a new class of statistical tests based on the concept of Newton's potential energy. Included in the package are * mvnorm.etest (test) and mvnorm.e (statistic) A rotation invariant multivariate goodness-of-fit test, implemented for testing

Good Night I made different test to check normality and multinormality in my dataset, but I don´t know which test is better. To verify univariate normality I checked: shapiro.test, cvm.test, ad.test, lillie.test, sf.test or jaque.bera.test and To verify multivariate normal distribution I use mardia, mvShapiro.Test, mvsf, mshapiro.test, mvnorm.e. I have a dataset with almost 1000 data and 9

is there any function under R that allows me to test the normality of my 92 sumples? -- View this message in context: http://www.nabble.com/multinormality-tp17717230p17717230.html Sent from the R help mailing list archive at Nabble.com.

Hello, I have a question about multivariate Shapiro-Wilks test. I tried to analyze if the data I have are multivariate normal, or how far they are from being multivariate normal. However, any time I did >mshapiro.test(mydata) I get the message: Error in solve.default(R %*% t(R), tol = 1e-18) : system is computationally singular: reciprocal condition number = 5.38814e-021 I tried

Hello all, I tried several experiments with the mshapiro.test package in R and compared it with the energy package to test for multivariate normality and find that the mshapiro.test is not consistent which is a bit concerning and has suspicious behavior. On the other hand the energy test seems to be a more appropriate test for testing multivariate normality in any dimension. I looked for the

Dear R-users, An updated version of the energy package, energy 1.1-0, is now available on CRAN. This version has merged the dcov package (previously available from my personal web page) into energy. New functions include: dcov (distance covariance) dcor (distance correlation) DCOR (four statistics) dcov.test (distance covariance test of multivariate independence) indep.test (choice of

Dear R-users, An updated version of the energy package, energy 1.1-0, is now available on CRAN. This version has merged the dcov package (previously available from my personal web page) into energy. New functions include: dcov (distance covariance) dcor (distance correlation) DCOR (four statistics) dcov.test (distance covariance test of multivariate independence) indep.test (choice of

Dear all, I have a multivariate dataset containing 100,000 or more points. I want find the p-value for the dataset of points coming from a particular multivariate normal distribution With mean vector u Covariance matrix s2 So H0: points ~ MVN( u, s2) H1: points not ~ MVN( u, s2) How do I find the p-value in R? To me this is a likelihood ratio test problem. In H0 the parameters are

Hi again. I'm putting together a little project with R, python, and a website. So I have an HTML file, a py file, an R file. Here is the HTML file: <form action="/cgi-bin/radio4.py" method="post" target="_blank"> <input type="radio" name="subject" value="Integrate" /> Integrate <input type="radio"

Hi I have a few problems with tail/head when applied to multiple time series. I'm not sure as whether I did not understand the function or whether it correspond to an unexpected behavior. When head(a,n) is applied on data.frame or matrix, it returns a data-frame or matrix with first n obs of *each* variable. When applied to a mts object, it returns first n obs of *first* variable only,

I'm trying to use garchFit from fSeries, with Student or Skewed Student conditionnal distribution. Let's say that eps (vector) is my series of daily log-returns: data(EuStockMarkets) eps = diff(log(EuStockMarkets[,"CAC"])) library(fSeries) g = garchFit(series = eps, formula.var = ~garch(2,2), cond.dist = "dstd") s = g at fit$series All the coefficients are ok

All, I am having trouble with a "read.table()" function that is inside of another function. But if I call the function by itself, it works fine. Moreover, if I run the script on a Mac OS X (with the default Mac OS X version of R installed, rev 2.8), it works fine. But it does not work if I run it on windows vista (also default Windows version of R, rev. 2.8). Again, both

Hello, I'm trying to get a few turnpoints for a financial time series. There is a function in pastecs that does that. However, I get a large number of turnpoints: library(pastecs) data(EuStockMarkets) dax <-EuStockMarkets[,1] plot(dax) turnp <-turnpoints(dax) summary(turnp) #gives 925 peaks/pits!!! How can specify to get only 30 turnpoints? Second question: the extract function

Dear R users, I need to estimate an ARCH(1,0) model with t-residuals. To do this I use garchFit function from fGarch library. However, I get the following error message: Error in.garchInitParameters (formula.mean = formula.mean, formula.var = formula.var, ): object "alpha" not found I tried to estimate this model with different series, but I always get this error message. For example,

Dear list-reader, by running the following script: library(zoo) sessionInfo() search() packageDescription("zoo") data(EuStockMarkets) dax <- as.zoo(EuStockMarkets[1:10, "DAX"]) daxr <- diff(log(dax)) identical(as.vector(qnorm(daxr)), qnorm(coredata(daxr))) qqnorm(coredata(daxr)) qqnorm(daxr) qqnorm() produces an error: > qqnorm(daxr) Fehler in if (xi == xj) 0L