similar to: Bivariate normal equal-probability curve...

OK, this should be trivial but I'm not finding it. I want to compress the test, if (i==7 | i==10 | i==30 | i==50) {} into something like if (i in c(7,10,30,50)) {} so I can build a "excludes" vector excludes <- c(7,10,30,50) and test if (i in excludes) {} However, I'm not finding a clue on how to accomplish this, if it can be done. Would someone with more R

Hi, I would like to plot a x,y curve described by the equation : Ax^2 + Bx + Cy^2 + Dy + E == 0 (A,B,C,D,E are constants) This sounds like quite the common task but haven't been able to figure out how to do this. Could you please help ? I am new to R so probably missing something basic. Thanks

Hi all, I am trying to build a copula model using the Gumbel Copula and I have two marginal distributions.I know the marginal parameters by using the fitdistr() and optim().The problem is I dont know my copula parameter. I am getting a bit confused of how shall I go about it.I read the previous threads where the query was to estimate all the parameters.How shall I define my log-likelihood

Thanks Dimitris!!! That's much clearer now. Still have a lot of work to do this weekend to understand every bit but your code will prove very useful. Cheers, Aziz -----Original Message----- From: Dimitrios Rizopoulos [mailto:Dimitris.Rizopoulos at med.kuleuven.be] Sent: May 12, 2006 4:35 PM To: Chaouch, Aziz Subject: RE: [R] Maximum likelihood estimate of bivariate

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]]

Hi, I'm dealing with wind data and I'd like to model their distribution in order to simulate data to fill-in missing values. Wind direction are typically following a vonmises distribution and wind speeds follow a weibull distribution. I'd like to build a joint distribution of directions and speeds as a VonMises-Weibull bivariate distribution. First is this a stupid question? I'm

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

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

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

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, 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

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

Hi, I woul like to know if it is possible to have a "R code" to generate EM Algorithm for a normal bivariate mixture. Best regard, S.F.

Hi.., is there a function in R to fit bivariate splines ? I came across 'polymars' (POLSPLINE) and 'mars' (mda) packages. Are these the one to use or are there other specific commands? Thanks. Harsh

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

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

Dear R-List: I`m trying to calculate the probabilities for a bivariate normal distribution while using the mvtnorm-package(dmvnorm) and the adapt-package for multidimensional integration. The problem is that I can`t specify the upper bound in the adapt-package the way I need it because I don`t need a rectangular area. I want to calculate the probability starting at the origin under the line y=x.

Hi, there. Does anyone know the R function for calculating the cdf of bivariate normal distribution function? Thanks. Yulei [[alternative HTML version deleted]]