similar to: save p-value in mshapiro.test(mvnormtest)

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.

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, 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

Can R invert a 160000x160000 matrix with all positive numbers? 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

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:

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.

My stat textbook tells me that using Shapiro-Wilk test for each variable one by one is not equal to a test for multivariate normality as a whole. Does R have a function of testing for multivariate normality? Thanks. Hongsheng (Hank) Liao, Ph.D. Lab Manager Center for Quantitative Fisheries Ecology 800 West 46th Street Old Dominion University Norfolk, Virginia 23508 Phone:757.683.4571

For example, the correlation matrix is 3x3 and looks like 1 0.75 0 0 0 0.75 1 0 0 0 0 0 0 0 0 Can I write the code like this? p<- 3 # number of variables per observation N<- 10 # number of samples # define population correlation matrix sigma sigma<-matrix(0,p,p) #creates a px p matrix of 0 rank<-2 for (i in 1:rank){ for (j in 1:rank){ rho<-0.75

o <- (structure(list(sand.silt = structure(list(statistic = structure(185, .Names = "W"), parameter = NULL, p.value = 0.0478835773838087, null.value = structure(0, .Names = "location shift"), alternative = "two.sided", method = "Wilcoxon rank sum test with continuity correction", data.name = ".column by site"), .Names =

Hello, I've posted a question about this subject yesterday, but since there was no R code to comment, no one did. I'm trying to have the print method for class 'htest' print some extra information common in some test, like the time series linearity related tests. Many of them have an 'order' parameter, representing a lag or embedding dimension, and it would be a nice

Hello! First of all I must appologize if this has been raised previously, but search provided by Robert King at the University of Newcastle seems to be down these days. Additionally let me know if such a question should be sent to R-help. I did a contribution to function hwe.hardy in package 'gap' during the weekend. That functions performs Hardy-Weinberg equilibrium test using MCMC. The

Hello, everyone, I don't quite understand the following message: > TTT <- t.test(1:10, y=c(7:20)) > class(TTT) [1] "htest" > TTT@p.value Error: Trying to get slot "p.value" from an object whose class ("htest") is not defined > TTT$p.value [1] 1.855282e-05 Why the message says the class of TTT is not defined while class(TTT) gets

Hi all, I just used power.t.test() in Version 1.9.0 beta (2004-03-31) today under FC1. The following is the output: > power.t.test(delta = .5, power = .95, type = "paired") $n [1] 53.94062 $delta [1] 0.5 $sd [1] 1 $sig.level [1] 0.05 $power [1] 0.95 $alternative [1] "two.sided" $note [1] "n is number of *pairs*, sd is std.dev. of *differences* within

Is there any automatic mechanism for extracting a likelihood or test statistic distribution (PDF or CDF) from an object of class "htest" or from another object of a general class encoding a hypothesis test result? I would like to have a function that takes "x", an object of class "htest", as its only argument and that returns the likelihood or test statistic

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

I'd like to learn the process of revising R functions & packages and then submitting proposed patches to the R Core team. Would someone be willing to mentor me through one example? For starters, consider an example. I'd like to revise the t.test function to return the stderr value to the user. We only need to change the "rval" in the third-from-the end line of

Dear all How does print.htest display the p-value in scientific notation? > (x <- cor.test(iris[[1]], iris[[3]])) Pearson's product-moment correlation data: iris[[1]] and iris[[3]] t = 21.65, df = 148, p-value < 2.2e-16 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: 0.8270 0.9055 sample estimates: cor 0.8718 Above the p-value comes

I sort of asked this before, but perhaps not explicitly enough. In my ctest collection, there are several chisquare-based tests. For some of them, it may be useful to also return information on expected (and observed) counts. The question is, how should this be done. Of course, there is no problem adding the corresponding components to the list returned by the functions. However, as these are

Hello, I am running Kruskal-Walis test in R. When I try to save results using write.table it gives me the following error : Error in as.data.frame.default(x[[i]], optional = TRUE, stringsAsFactors = stringsAsFactors) : cannot coerce class "htest" into a data.frame The overall code is as follows : >data_file = read.table("~/DATA.dir/data_file.txt", header=T)