similar to: Generating a valid covariance matrix

Dear All, The function curve() draws the graph of functions from R to R. Is there some homologous function to curve() to draw functions from R^2 to R? Thanks in advance, Paul

Well, this is what i got... > -0.0841219200008394^(1/3) [1] -0.438163696867656 > (-0.0841219200008394)^(1/3) [1] NaN and i don't have a clue of why this happens or how to avoid it, any suggestions? thank you, Juan

Hi R-helpers, I have two queries relating to generalising to n dimensions: What I want to do in the first one is generalise the following statement: expand<-expand.grid(1:x[1],1:x[2],...1:x[n]) where x is a vector of integers and expand.grid gives every combination of the set of numbers, so for example, expand.grid(1:2, 1:3) takes 1,2 and 1,2,3 and gives 1,1 2,1 1,2 2,2 1,3 2,3 My x

Dear R users, I am doing MCMC using Metropolis-Hastings. My model is bivariate-log-normal and the prior for variance-covariance is wishart distribution. I am wondering if there are some simple codes about how to get the density of Wishart distribution in my case ? Thanks in advance. Meihua [[alternative HTML version deleted]]

Hi I am having difficulty defining an S4 method for head() and tail(). I can't quite provide minimal self-contained code because the problem appears to require the whole corpus of my package; and there also appears to be a difference between sourcing the lines directly, and having them installed in a package. The lines in question (I think) are: setClass("mdm",

Hello Everyone Both the MCMCpack and the bayesm libraries allow us to make draws from the Inverse Wishart distribution. But I wanted to find out how exactly is the Inverse Wishart distribution parameterized in these libraries. The reason I ask is the following: Now its generally standard to express Inverse Wishart as IW(0.5 * DOF,0.5* Scale). (DOF-> Degree of freedom, Scale -> Scale

Hello everyone I was wondering how would one simulate from a Normal Wishart Distribution in R. A normal inverted Wishart distribution is denoted by NIW (M,C,d,S), where X/(Sigma) ~ N( M,C,(Sigma) ) -> a matrix normal distribution, (Sigma) -> column dispersion matrix (Sigma) ~ IW (d,S) -> inverted Wishart distribution Thanks a lot ! Best Shantanu [[alternative HTML version

Hi Given three vectors x <- c(fish=3, dogs=5, bats=2) y <- c(dogs=1, hogs=3) z <- c(bats=3, dogs=5) How do I create a multi-way table like the following? > out x y z bats 2 0 3 dogs 5 1 5 fish 3 0 0 hogs 0 3 0 ('out' is a matrix). See how the first line shows 'x' has 2 bats, 'y' has zero bats, and 'z' has 3 bats and so on for each

Hi, I am not sure if this make sense at all. I'd like to sample a matrix, which follows a wishart / inverted wishart distribution. However, the (1,1) element of this matrix should always be equal to 1. How can I handle it in R? Any suggestion is greatly appreciated. Thanks a lot. Sonia [[alternative HTML version deleted]]

Hi I read that bundles are to be deprecated in 2.10. The BACCO bundle contains three packages (emulator, calibrator, approximator) which I am happy to unbundle. But the 'BACCO' moniker has some considerable cachet for me in terms of recognizability (eg with grant-giving bodies), as it has become an umbrella term for a whole bunch of related statistical functionality of which the three

I have 2 vecros : x<-c(100,96,88,100,100,96,80,68,92,96,88,92,68,84,84,88,72,88,72,88) x1 = sample(x, 5, replace=FALSE) Now i want to get remaining values of vector "x" those are not member of vector "x1". Can anyone please tell me how to do that?

Thanks David, It is working. Holtman's also gave me a solution but, I wanted to have a color pallet for description of colors, that was not in his solution. However I need one small modification. If I want to plot only lower diagonal elements of 'dat' then how should I proceed? What I want is, to visualize only lower diagonal elements and having the color pallet on them only. Also

Hello everyone. R-2.12.0, suse linux 11.3. I am debugging a package that uses S4 methods and R CMD check gives the following warning: > Warning in methods::findMethods(g, env) : > non-generic function 'mdm' given to findMethods() > See the information on DESCRIPTION files in the chapter 'Creating R > packages' of the 'Writing R Extensions' manual. I

R users- In moving from R 2.0.0 to R 2.1.0 in Windows, I have encountered a problem with the "riwish" command in the package "MCMCpack". I've searched the documentation and can't seem to figure it out. For example: Define a matrix: > lam <- matrix(c(.00233,-.00057,-.00057,.00190),2,2) and then use the riwish command to generate a random inverse-Wishart

Dear all R users, Please forgive me if my question is too trivial. Suppose I have two variables, (x,y) which is log-normally distributed with expected value (mu1, mu2) and some variance-covariance matrix. Now I want to draw a random sample of size 1000 from this distribution. Is there any function available to do this? Thanks and regards, Megh

Hi suppose I have a factor 'x': > x <- as.factor(c(rep("a",3),"b","d")) > table(x) x a b d 3 1 1 > > But this is not what I want because I need to include the fact that the count of "c" is zero. I can't just change the levels of x: > levels(x) <- c("a","b","c","d") >

Let me take an artifical matrix : dat = matrix(rnorm(200*200), 200, 200) My goal is to visualize this matrix according to the procedure, described in previous mails. I took Mendelssohn's advice and got following advice : ?plot.im Z <- setcov(owin()) plot(Z) .................... etc However I can not reproduce this example in my problem. How I can change my data

Hi I want a generalization of tabulate() which works on rows of a matrix. Suppose I have an integer matrix 'observation': > observation y1 y2 y3 1 4 0 1 4 0 2 0 3 4 1 0 0 5 0 0 1 4 2 0 3 Each row corresponds to a (multivariate) observation. Note that the first two rows are identical: this means that data "c(1,4,0)" was observed twice. Now suppose I can list the sample

Dear R users, I would like to make my R code for MCMC faster. It is possible to integrate C code into R but I think C is too complicated for me. I would need a C introduction only for MCMC and I do not know if such a thing exists. I was thinking of Python (and scipy). Where could I read about its integration into R ? How developed are the statistical packages in Python ? I could not find a

Dear all R users, I have a small doubt about panel data analysis. My basic understanding on Panel data is a type of data that is collected over time and subjects. Vector Autoregressive Model (VAR) model used on this type of data. Therefore can I say that, one of statistical tools used for analysis of panel data is VAR model? If you clarify my doubt I will be very grateful. Thanks and regards,