similar to: question for mshapiro 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!

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, Could someone help me to explain the VERY big difference in applying two tests on multivariate normality: library(mvnormtest) data(EuStockMarkets) mshapiro.test(t(EuStockMarkets[15:29,1:4])) Shapiro-Wilk normality test data: Z W = 0.8161, p-value = 0.005955 and library(energy) mvnorm.etest( EuStockMarkets[15:29,1:4] ) Energy test of multivariate normality:

Dear All, I would be very grateful for your help concerning the following question: Below mentioned programme is available on net to generate longitudinal data. Usually we get almost same parameter estimates as used to generate the data. The problem here is I am not able to get it for data used here, despite increasing sample size and number of simulations. Is it normal to expect this type of

Dear friends, regression towards the mean is interesting in medical circles, and a very recent paper (The American Statistician November 2007;61:302-307 by Krause and Pinheiro) treats it at length. An initial example specifies (p 303): "Consider the following example: we draw 100 samples from a bivariate Normal distribution with X0~N(0,1), X1~N(0,1) and cov(X0,X1)=0.7, We then calculate

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

You'll find that it is a lot easier to do it in R: # lets first simulate a bivariate normal sample library(MASS) bivn <- mvrnorm(1000, mu = c(0, 0), Sigma = matrix(c(1, .5, .5, 1), 2)) # now we do a kernel density estimate bivn.kde <- kde2d(bivn[,1], bivn[,2], n = 50) # now plot your results contour(bivn.kde) image(bivn.kde) persp(bivn.kde, phi = 45, theta = 30) # fancy contour with

Hello, I used the following codes to generate bivariate normal dependence structure with unit Frechet margins. Sigma <- matrix(c(1,.5*sqrt(1),.5*sqrt(1),1),2,2) # generate y <- mvrnorm(Nsam, c(0,0), Sigma) # random v <- cbind(pnorm(y[,1],mean = 0, sd = 1), pnorm(y[,2],mean = 0, sd = 1)) z <- cbind(-1/log(v[,1]),-1/log(v[,2])) z1 <- z[,1] z2 <- z[,2] And to

Hi there, I would like to draw 10 correlations from a bivariate population - but every draw should be done with a different sample size. I thought I could to this with a loop: r=numeric(10) #Goal vector N = c(1000,100,80,250,125,375,90,211,160,540) #Sample size vector for(i in 1:10) { data <- mvrnorm(n=N,mu=c(0,0),Sigma=matrix(c(1,.3,.3,1),2)) r[i] <- cor(data[,1],data[,2]) }

Dear R-help, I'm using the 'optim' functions to minimise functions, and have read the documentation, but I'm still not sure how to determine sensible values to use for the 'fnscale' and 'parscale' options. If I have understood everything correctly, 'fnscale' should be used to scale the objective function, so that for example if the default is

Hello R-users! I am trying to plot 3 vectors (x,y,z) of observations generated by mvrnorm in library(MASS). I tried plot3d in library(djmrgl) and scatterplot3d. But these program gives x,y,z axis which do not intersect at the origin (0,0,0). I searched through all the graphics related packages for R like xgobi, ggobi, grid and more. Quite confusing which one to choose. Would anyone recommend

Hi, this is simple, I've generated a bivariate normal distribution with a known correlation. I want to plot the density function with p(y,x) on the vertical axis and x, and y on the horizontal axes. How is that done? Thanks in advance! library(MASS) mu <- c(0,1) # mean vector sigma <- matrix(c(1,.8,.8,1), ncol=2) tmp <- mvrnorm(1000, mu, sigma) cor(tmp)

x <- iris$Sepal.Length[1:50]/iris$Sepal.Width[1:50] y <- iris$Petal.Length[1:50]/iris$Petal.Width[1:50] I want to check whether (x,y) follows a bivariate normal distribution or not, using density plot or scatter plot. Is it possible to plot a bivariate density in R. I cant find any. Arindam Fadikar M.Stat Indian Statistical Institute. New Delhi, India [[alternative HTML version

> Date: Wed, 12 Jan 2000 13:05:21 +0100 (CET) > From: Adriane Leal <loparic at student.fsa.ucl.ac.be> > To: r-help at stat.math.ethz.ch > Subject: [R] Problems creating a random sample > > Hi! > > I'm supposed to create a random sample bivariate normal variates of size > equal to 250 with mean vector (-1,1) and cov matrix (1,0.95,0.95,1) > > In S

dear list, as a part my problem. I have to estimate some parameters using ML estimation. The form of the likelihood function is not straight forward and I had to use a for loop to define the function. I used "optim" to maximise the result but was not sure of the programme. To validate my results, I tried to write a function to obtain the MLE of a bivariate normal in the same manner. On

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

Hi all, I understand that it is simple to create data with a specific correlation (say, .5) using mvrnorm from the MASS library: > library(MASS) > set.seed(1) > > a=mvrnorm( + n=10 + ,mu=rep(0,2) + ,Sigma=matrix(c(1,.5,.5,1),2,2) + ,empirical=T + ) > a [,1] [,2] [1,] -1.0008380 -1.233467875 [2,] -0.1588633 -0.003410001 [3,] 1.2054727 -0.620558768

Dear experts! Is there a package that enables to create the bivariate log-normal variables? Thanks a lot, Vicky Landsman. [[alternative HTML version deleted]]

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

Hello! How can I do 100 simulations of length 17 from bivariate bivariate normal distribution, if I know all 5 parameters? -- View this message in context: http://r.789695.n4.nabble.com/Simulation-in-R-tp4651578.html Sent from the R help mailing list archive at Nabble.com.