similar to: Plot bivariate joint pdf

HI Guys, I know that this forum is not for homework but I am trying to interpret R output code. I was just wondering if someone might be able to help. I have been given the following. For (X1,X2) distributed bivariate normal with parameters mu1 = 5.8 mu2 = 5.3 sd1 = sd2 = 0.2 and p = 0.6 The r-code and inpit/output are as follows input m <- 5.3 + 0.6*(6.3 - 5.8) s <-

Hi Guy's I was wondering if someone could point me in the right direction. dbinom(10,1,0.25) I am using dbinom(10,1,0.25) to calculate the probabilty of 10 judges choosing a certain brand x times. I was wondering how I would go about simulating 1000 trials of each x value ? regards Brendan -- View this message in context:

Hello, I am using sm.density() to find the bivariate joint PDFof events: For eg, x<-cbind(rnorm(30),rnorm(30)) den<-sm.density(x) Then I get the joint pdf from den$estimate in order to constructthe joint cumulative PDF. However, summing up all the values from den$estimateisnot equal to 1(have multipliedby the grid size). Anyone could help? Thanks. mc [[alternative HTML version

Hi, I conduct a panel data estimation and obtain estimators for two of the coefficients beta1 and beta2. R tells me the mean and covariance of the distribution of (beta1, beta2). Now I would like to find the distribution of the quotient beta1/beta2, and one way to do it is to simulate via the joint distribution (beta1, beta2), where both beta1 and beta2 follow t distribution. How could we

Dear all, I have a bivariate dataset from a preliminary study. I want to do two things: (1) estimate the probability density of this bivariate distribution using some nonparametric method (kernel, spline etc); (2) sample a big dataset from this bivariate distribution for a simulation study. Is there any good method or package I can use in R for my work? I don?t want parametric models like

Dear helpers, I was wondering if there are some bivariate distribution simulation functions in any R packages. Specifically, I want to simulate bivariate log logistic, bivariate Weibull, or other common bivariate life time functions, and I can specify the correlations. Any comments about this will be appreciated. Thanks, Zhu Wang

"Jenny Stadt" <jennystadt at yahoo.ca> asked: > > I am struggling in a bivariate Weibull distribution although I > searched R-Site-Help and found suggestion with Copula. Seems the > maximum likelihood estimate is beyond what I can understand. > > My case is: given two known marginal distribution (both are Weibull), > and the correlation between them. How can I

Respected R Users, I looking for help with generating theoretical confidence regions for any of non-symmetric bivariate statistical distributions (bivariate Chi-squared distribution<Wishart distribution>, bivariate F-distribution, or any of the others). I want to to used it as a benchmark to compare a few strategies constructing confidence regions for non-symmetric bivariate data. There is

Hello, I'm quite new with R and so I would like to know if there is a command to calculate an integral. In particular I simulated a bivariate normal distribution using these simple lines: rbivnorm <- function(n, # sample size mux, # expected value of x muy, # expected value of Y sigmax, # standard deviation of

Am trying to do some study on bivariate binomial distribution. Anyone knows if there is package in R that I can use to calculate the density function of bivariate binomial distribution and to generate random samples of it. Thanks, -- View this message in context: http://n4.nabble.com/How-to-calculate-density-function-of-Bivariate-binomial-distribution-tp992002p992002.html Sent from the R help

Hello everyone, I've been looking for an R function to calculate bivariate weighted fits of my data set, preferably using methods of Williamson-York. Improvements offered by using bivariate weighted fitting compared to conventional linear least-square fitting was recently described in a paper by Cantrell (http://www.atmos-chem-phys.org/8/5477/2008/acp-8-5477-2008.pdf), in which the methods

Hi all, I am using the function COV.WT to estimate the estimators (location and scale) of a bivariate cauchy distribution. My doubt is about the option WT (weight), cause at the R-help shows that the weight is uniform according to the number of observations. But, checking the theory, for example, the mean is given by mean_estimator=mean(u(s)x)/mean(u(s)), where x=my data (bivariate)

Good morning and I appreciate the availability of a help-list. I am a professional hydrologist, but not a professional statistician. Yet I find myself using statistical tools at least part of the time. My discovery of the R-project through a friend has been most helpful. Here is my problem: I'm tasked with fitting a dataset comprising correlated discharges from adjacent watersheds to

Suppose F(x, y; rho) is the cdf of a bivariate normal distribution, with standardized marginals and correlation parameter rho. For any fixed x and y, I wonder if F(x, y; rho) is a monotone increasing function of rho, i.e., there is a 1 to 1 map from rho to F(x, y; rho). I explored it using the function pmvnorm in package mvtnorm with different x and y. The plot suggests the statement may be true.

Hi: I am using the package "KernSmooth" to do the local polynomial regression. However, it seems the function "locpoly" can only deal with univariate covaraite. I wonder is there any kernel smoothing package in R can deal with bivariate covariates? I also checked the package "lcofit" in which function "lcofit" can indeed deal with bivariate case. The

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

Any one know is there any package or function to generate bivariate exponential distribution? I gusee there should be three parameters, two rate parameters and one correlation parameter. I just did not find any function available on R. Any suggestion is appreciated. -- View this message in context:

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

Hello all!!! 1) I am wondering is there a way to generate random numbers in R for Bivariate Uniform distribution? 2) Does R have  built-in function for generating random numbers for any given bivariate distribution. Any help would be greatly appreciated !! Good day! Haneef Anver [[alternative HTML version deleted]]

Hello, I have used the univariate Ripley K function in R, but does anyone know if there is a bivariate function built in? I have two species that I am dealing with. Also, how might I add error bars into the graphs (univariate and/or bivariate)? Thank you, Karin Leiderman k_leiderman at hotmail.com Graduate Student/Research Assistant Department of Mathematics Univesity of New Mexico