similar to: Stats Question: Single data item versus Sample from Normal Distribution

Here's one possibility, assuming muhat and sigmahat are estimtes of mu and sigma from N iid draws of N(mu, sigma^2): tStat <- abs(x - muhat) / sigmahat pValue <- pt(tStat, df=N, lower=TRUE) I'm not quite sure what df tStat should have (exercise for math stat), but given fairly large N, that should make little difference. Andy > From: Ross Clement > > Hi. I have a

Dear R users, I would like to estimate mu and sigma of a log-normal distribution, where I know that the expected value is 1, as it is a normalized distribution. That means as E(x) = exp (mu + 1/2*sigma^2) = 1 that 2*mu = -sigma^2 . Therefore I only need to fit one parameter either sigma or mu. How could I do this in R? Thank you very much for your help! biophil [[alternative HTML version

Dear R users,   I need to do a F test on the hypothesis that a 2 by 1 vector (X_1, X_2)' has the mean vector  (M_1, M_2)'. Specifically, I would like to assume the X vector comes from a bivariate Normal distribution (M, Sigma). Then, given 1000 observations on X, I wanted to test if the means of X agree with the means of the target Normal distribution. Any function or package in R could do

Hi, guys, I am trying to sample from a truncated normal/gamma distribution. But only the far end of the distribution (where the probability is very low) is left. e.g. mu = - 4; sigma = 0.1; The distribution is Normal(mu,sigma^2) truncated on [0,+Inf]; How can I get a sample? I tried to use inverse CDF method, but got Inf as answers. Please help me out. Also, pls help me on the similar

Dear list, I have a dataset containing values obtained from two different instruments (x and y). I want to generate 5 samples from normal distribution for each instrument based on their means and standard deviations. The problem is values from both instruments are non-negative, so if using rnorm I would get some negative values. Is there any options to determine the lower bound of normal

Hi: Does Ellipse and dataellipse function in R produce the same ellipse? I wanted to see how the radius for the Ellipse function in R calculated. Also what is the var-covariance matrix, if any, assumed for the dataellipse function? Heres an example of the code where I am generating Multivariate normal data and creating ellipse using the 2 functions: library(car) library(mvtnorm) mu =

I?m working on an R-implementation of the simulation-based finite-sample null-distribution of (R)LR-Test in Mixed Models (i.e. testing for Var(RandomEffect)=0) derived by C. M. Crainiceanu and D. Ruppert. I'm in the beginning stages of this project and while comparing quick and dirty grid-search-methods and more exact optim()/optimize()-based methods to find the maximum of a part of the

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

I would like to be able to place a normal distribution surrounding the predicted values at various places on a plot. Below is some toy code that creates a scatterplot and plots a regression line through the data. library(MASS) mu <- c(0,1) Sigma <- matrix(c(1,.8,.8,1), ncol=2) set.seed(123) x <- mvrnorm(50,mu,Sigma) plot(x) abline(lm(x[,2] ~ x[,1])) Say I want to add a normal

Hello, My data is discrete, taking values between around -5 and +5. I cannot give bounds for the values. So I consider it as numerical data and not categorical data. The histogram has a 'normal' shape, so I test for normality via a chisquare statistic (by calculating the expected values by hand). When I use the sample mean and variance, the normality hypothesis has to be rejected.

¡Hola a todos! Estoy intentando muestrear de una normal multivariante donde hay dos grupos de variables que deben tener una relación "manipulable" entre sí pero ignoro cómo hacerlo. Les cuento, he intentado lo siguiente: # covarianzas del primer grupo de variables: Sigma_U <- matrix(c(.25, .2, .2, .25), ncol=2) # covarianzas del segundo grupo de variables: Sigma_W <- diag(2) #

Sorry about the subject --- On Thu, 24/9/09, KABELI MEFANE <kabelimefane@yahoo.co.uk> wrote: From: KABELI MEFANE <kabelimefane@yahoo.co.uk> Subject: Re: [R] Multiply Normal Curves To: R-help@r-project.org Date: Thursday, 24 September, 2009, 11:48 AM R -helpers   i have been trying to do this problem without must success,i managed to do a graph for x, but it is not what i want to

Hello, I would like to simulate randomly from a multivariate normal distribution using a correlation matrix, rho. I do not have sigma. I have searched the help archive and the R documentation as well as doing a standard google search. What I have seen is that one can either use rmvnorm in the package: mvtnorm or mvrnorm in the package: MASS. I believe I read somewhere that the latter was

Does anyone know of a package that uses C code to calculate a multivariate normal density? My goal is to find a faster way to calculate MVN densities and avoid R loops or apply functions, such as when X and mu are N x K matrices, as opposed to vectors, and in this particular case, speed really matters. I would like to be able to use .C or .Call to pass X, mu, Sigma, and N to a C program and have

Tom Cohen <tom.cohen78@yahoo.se> skrev: Thanks Prof Brian for your suggestion. I should know that for right-skewed data, one should generate the samples from a lognormal. My problem is that x and y are two instruments that were thought to be measured the same thing but somehow show a wide confidence interval of the difference between the two intruments.This may be true that these

Hello. Somebody knows how can I generate a set of n random vectors of a normal distribution of several variables? For example, I want to generate n=100 random vectors of two dimensions for a normal with mean c(0,1) and variance matrix: matrix(c(2,1,1,3),2,2). Thanks in advance, Arnau.

Greetings: I would like comments on this example and after fixing it up, I need help from someone who has access to insert this in R's help page for plotmath. I uploaded a drawing http://pj.freefaculty.org/R/Normal-2009.pdf that is created by the following code http://pj.freefaculty.org/R/Normal1_2009_plotmathExample.R This will be a good addition to the plotmath help page/example.

Here's another example of my plotmath whipping boy, the Normal distribution. A colleague asks for a Normal plotted above a series of axes that represent various other distributions (T, etc). I want to use vectors of equations in plotmath to do this, but have run into trouble. Now I've isolated the problem down to a relatively small piece of working example code (below). If you would

Hi Folks! Just started using the gamlss package and I tried a simple code example (see below). Why the negative sigma? John > y <- rt(100, df=1)> m1<-fitDist(y, type="realline")Warning messages:1: In MLE(ll3, start = list(eta.mu = eta.mu, eta.sigma = eta.sigma, : possible convergence problem: optim gave code=1 false convergence (8)2: In MLE(ll4, start = list(eta.mu =

Dear R list, I would like to illustrate the origin of the Student t distribution using R. So, if (sample.mean - pop.mean) / standard.error(sample.mean) has t distribution with (sample.size - 1) degree free, what is wrong with the simulation below? I think that the theoretical curve should agree with the relative frequencies of the t values calculated: #== begin options===== # parameters