search for: mvnorm

...ions: > search() [1] ".GlobalEnv" "package:e1071" "package:ctest" "Autoloads" [5] "package:base" > objects(pos=2) and comparing to the ones listed in library/e1071/help/AnIndex, I've found that the missing ones are: channel.pnm Mvnorm plot.clobj pnm read.pnm write.pgm As it is often the case, the missing ones are the ones I need... I've checked that this is not a downloading problem, the CRAN file: http://cran.r-project.org/bin/windows/contrib/e1071.zip is identical to the one I have on my disk. Maybe a problem at compi...

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 mvnorm.etest. I would like to know the better way to solve this problem, as well as doing it faster. Best regards, Pedro Marques

Hi All, I am new in R and trying to simulate random normal 2D field with mean trend say north-south. My domain is 10x10 grid and I am trying to use mvnorm but do not know how to specify the domain and the mean field. I would appreciate any help. Cheers, SK --------------------------------- [[alternative HTML version deleted]]

...k 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 variables, in both cases the result is non-normality. For this reason, I transformed data with bcPower function and I want to check normality again. I really appreciate your help. Thanks. [[alternative HTML version deleted]]

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 multivariate normality with estimated parameters * eqdist.etest (test) and ksample.e (statistic) A k-sample multivariate nonparametric test of equal dis...

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 multivariate normality with estimated parameters * eqdist.etest (test) and ksample.e (statistic) A k-sample multivariate nonparametric test of equal dis...

Dear Sir, Yes, I want to generate the data by two Gaussian but mean from one of them generatated from polar its coordinates. I am confused about the algorithm? I know how to generate two Gaussian data with certain mean&covariance matrix using mvnorm but what about mean from its polar coordinates? Thanks for your help. Best wishes, Iut ------Pesan Asli------ Dari: John Kane Ke: Iut Tri Utami Ke: r-help at r-project.org Perihal: RE: [R] example Terkirim: 27 Apr 2013 8:19 PM https://github.com/hadley/devtools/wiki/Reproducibility http://stack...

...e (in that there are R packages that do exactly what I want), but this code illustrates the problem. Thanks in advance for any help you can provide. Suppose I want to compute the log density of a multivariate normal distribution using C code and the gsl library. My R program is: dyn.load("mvnorm-logpdf.so") x<-c(0,0,0,0,0,0) mu<-c(0,0,0,0,0,0) sig<-diag(6) print(sig) w<-.Call("R_mvnorm_logpdf",as.double(x),as.double(mu),sig, as.integer(6)) print(sig) # sig has changed after .Call This code takes the SEXP's that were passed from R and converts them to...

...solutions with others), nor does it have the?Sweave option for standard reports like R does that can be distributed for free.?Unfortunately in R I am having a?hard time figuring the solution out.?I have tried to use the multivariate normal distribution function?mvrnorm from the MASS package, or the?Mvnorm from?mvtnorm package, but will get negative values simulated, which I can not afford, also, at times the simulated means, medians and?SDs are quiet different from what I started with (which may be due to the assumption I make?with regards to the distribution of the data). I was wondering if?anyone...

...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 language I would write: > > > mean<-c(-1,1) > > cov<-c(1,0.95,0.95,1) > > covmat<-matrix(cov,nrow=2,ncol=2) > > mvnorm<-rmvnorm(250,mean,covmat) > ... and it should work > > I've tried this in R, but it does not recognize "rmvnorm". > > (Error: couldn't find function "rmvnorm") rmvnorm is an S-PLUS function, version 4.0 and later. It is not part of S. It is based on...

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: estimated parameters data: x, sampl...

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.

...variance.model = list(garchOrder = c(1,1), model = "sGARCH"), distribution.model = "norm") > dcc.garch11.spec = dccspec(uspec = multispec( replicate(2, garch11.spec) ), dccOrder = c(1,1), distribution = "mvnorm") > fit.a = dccfit(dcc.garch11.spec, data = datax[,c(2,3)], out.sample = 500, fit.control = list(eval.se=T)) > dcc.focast=dccforecast(fit.a, n.ahead = 1, n.roll = 499) May I know how to get the forecast values from 'dcc.focast' ? I have given 500 as...

...variance.model = list(garchOrder = c(1,1), model = "sGARCH"), distribution.model = "norm") > dcc.garch11.spec = dccspec(uspec = multispec( replicate(2, garch11.spec) ), dccOrder = c(1,1), distribution = "mvnorm") > fit.a = dccfit(dcc.garch11.spec, data = datax[,c(2,3)], out.sample = 100, fit.control = list(eval.se=T)) > dcc.focast=dccforecast(fit.a, n.ahead = 100) May I know how to get the forecast values from 'dcc.focast' ? when i plot the model using, &g...

...quot;eGARCH",garchOrder=c(1,1)), distribution.model="norm",mean.model=list(armaOrder=c(0,0),include.mean=F)) model=multispec(replicate(2,uspec)) # replicate for two series # specify ADCC model mvspec=dccspec(model,dccOrder = c(1,1),model = "aDCC",distribution = "mvnorm") # cluster cl= makePSOCKcluster(2) multf = multifit(model, Data, cluster = cl) fitADCC=dccfit(mvspec,data = Data, fit = multf, cluster = cl) ADCC<-print(fitADCC) stopCluster(cl) # stop cluster # store covariance matrix COV<-matrix(rcov(fitADCC)[1,2,]) # COV conta...

Hola! Estoy teniendo un problema para testar la normalidad y la homogeneidad de varianzas para mi modelo de manova Es este mi modelo: m1<-manova(cbind(pred,fit,oniv)~tratamento/parcela+bloco,data.vegetacao) Aparte de tener tres variables respuestas (predador, fitófago, omnívoro), también es un diseño anidado. Alguien sabría responder esto? Gracias! Carol [[alternative HTML version

Dear list members I need a function that calculates the bivariate normal distribution for each observation. It is part of a likelihood function and I have 1000's of cases. As I understand it I cannot use packages like "mvtnorm" because it requres a covariance matrix of the same dimension as the number of observations. Basically what I need is a function that takes as arguments a

Please, I would like to know how to generate a truncated multivariate normal distribution k - dimensional, X ~ NT(mu, Sigma), where the elements of X to be non-negative (except the first), and the first dimension is strictly larger than zero. Example: X ~ NT_2(mu, Sigma), where mu=c(0.5, 0.5) and Sigma=c([120, 191], [191,154]), with X_1>0 and X_2>=0 Could anybody help

Hi, Thank you for the tip. I tried to re-intall R from Debian "stable", in which R's version is 1.4.0. And the installation of "mvtnorm" works. I then re-installed R yet again from Debian "unstable" (woody), in which R's version is 1.5.1. The installation of "mvtnorm" fails again with the same error message. Another package that failed with the

Hi, I have a new (for me) situation. I have a function for one linear model: y = 9.7909-0.035*x I have the variance of system var = 13.01403 Is possible to create a random vector points of Y (simulating the real data) over this curve using the function and the variance? I need this to a discipline. I have this: | \ | \ -> y = 9.7909-0.035*x and var = 13.014 | \ | \ | \ | \