similar to: 3d polar coordinates in rgl function view3d()

Context: I have two or more rgl-based views of a given data set, perhaps fitting different models, or showing different things across views. I want to be able to hand-rotate, zoom, scale one view to something I like, and then show the other views with matching viewpoints and scaling. so that one could flip back/forth among graphs and see only the relevant differences. In 2D, all this usually

Is there a way to change the viewpoint using view3d (or rgl.viewpoint) with respect to the image that currently appears rather than the perspective that rgl thinks is the default? For example, if I create an image and then perform what should be a no-op: rgl.viewpoint(userMatrix=rotationMatrix(0,1,0,0)) the image is rotated. Then I can perform rotations on the result without surprises. But I

Dear R community (and Duncan more specifically), I can't work out how to make additional light sources work in rgl. Here is the example. First I create a cube and visualize it: > cubo <- cube3d(col="black") > shade3d(cubo) Next I position the viewpoint at theta=0 and phi=30: > view3d(theta=0,phi=30) Next, I want to create a 2nd light source which diffuses red light

Hi, I'd like to plot in in polar coordinates a line which is given as a vector of lengths and angles. library("plotrix")

Hello, I have data for contour plot which is given in polar coordinates. Is there a straightforward way to plot in polar coordinates in R, or do i need to fiddle with transformations and then to sort the x and y vectors to be in ascending order as required by contour function and relatives? Cheers, Todor

Folks- I have developed a package which I am planning on posting to CRAN. I include examples for each of the 40 or so functions in the package. However, since the examples are non-trivial and the package is using MCMC simulation methods, it takes about 20 minutes to run the complete set of examples on a 2.8 Ghz windows PC. The R process reaches a high water mark of about 100 MB of memory as

Greetings to you all. I have two datasets - Time and magnitude. For a particular location, the magnitude of the parameter varies with time. I wish to obtain a polar coordinate distribution of time (0-24h) and magnitudes so as to visualize how magnitude varies with different times of the day (e.g., morning, midnight hours). I have searched for "polar coordinates in R" but could not get

I can successfully "check" a package with source under 2.5.1, including compiling source files and running examples with no errors or warnings. when I try with R2.6.2, I get make errors: making bayesmc.d from bayesmc.c make[3]:gcc-sjlj: Command not found etc. my gcc is version 3.4.2 I'm using Windows XP. Any thoughts? thanks! peter r ................................ Peter

Dear R users, The following issue has been already documented, but, if I am not mistaken, not yet solved. This issue appears while trying to plot arrows with "geom_segment" (package ggplot2), with polar coordinates ("coord_polar"). The direction of some arrows is wrong (red rectangle). Please find herewith an example. Does someone know how to deal with that issue? Best

Dear r-help- >From reading the CRAN web page, it appears that you should not submit precompiled binary versions of your package, but rather that these are built for you by someone working with CRAN. I submitted my package using R CMD build but without the binary flag on. I'd like to have a binary version available for Windows users. Should I submit a precompiled binary version as well?

We are pleased to announce the release of version 0.0 of bayesm on CRAN. bayesm covers many important models used in marketing and micro-econometrics applications. The package includes: Bayes Regression (univariate or multivariate dep var) Multinomial Logit Multinomial and Multivariate Probit Multivariate Mixtures of Normals Hierarchical Linear Models with a normal prior and covariates

We are pleased to announce the release of version 0.0 of bayesm on CRAN. bayesm covers many important models used in marketing and micro-econometrics applications. The package includes: Bayes Regression (univariate or multivariate dep var) Multinomial Logit Multinomial and Multivariate Probit Multivariate Mixtures of Normals Hierarchical Linear Models with a normal prior and covariates

Folks- a patch release of bayesm, v0.0-1, is now available on CRAN. This release corrects some errors in the help pages as well as one error in the function rhierLinearModel involving an incorrect default prior setting. peter ................................ Peter E. Rossi Joseph T. and Bernice S. Lewis Professor of Marketing and Statistics Editor, Quantitative Marketing and Economics Rm

Version 1.0-1 of bayesm is now available on CRAN. This is our first "production" version which include s much improved documentation as well as five data sets used in our book, Bayesian Statistics and Marketing. peter r ................................ Peter E. Rossi Joseph T. and Bernice S. Lewis Professor of Marketing and Statistics Editor, Quantitative Marketing and Economics

Version 1.1-0 of bayesm is now available on CRAN. This version includes Bayesian inference for the NBD(Poisson) regression model and a hierarchical version of the same. It also includes an additional dataset with count data and various covariates. Comments and suggestions for improvement are most welcome. ................................ Peter E. Rossi Joseph T. and Bernice S. Lewis

Folks- a patch release of bayesm, v0.0-1, is now available on CRAN. This release corrects some errors in the help pages as well as one error in the function rhierLinearModel involving an incorrect default prior setting. peter ................................ Peter E. Rossi Joseph T. and Bernice S. Lewis Professor of Marketing and Statistics Editor, Quantitative Marketing and Economics Rm

Version 1.0-1 of bayesm is now available on CRAN. This is our first "production" version which include s much improved documentation as well as five data sets used in our book, Bayesian Statistics and Marketing. peter r ................................ Peter E. Rossi Joseph T. and Bernice S. Lewis Professor of Marketing and Statistics Editor, Quantitative Marketing and Economics

Version 1.1-0 of bayesm is now available on CRAN. This version includes Bayesian inference for the NBD(Poisson) regression model and a hierarchical version of the same. It also includes an additional dataset with count data and various covariates. Comments and suggestions for improvement are most welcome. ................................ Peter E. Rossi Joseph T. and Bernice S. Lewis

Folks- We are pleased to announce the release of version 2.0-1 of bayesm. Highlights of the new version: 1. Bayesian treatment of SUR (seemingly unrelated regression) 2. Added clustering to mixture of normals models 3. Added routines to compute implied univ and bivariate densities from mixture of normals MCMC draws 4. improved input error checking in many routines please get rid of your

bayesm version 2.2-0 is now available on CRAN. Major changes include: 1. general density estimation using a Dirichlet Process Prior and a normal base 2. linear instrumental variable models with unknown error distributions (the Bayesian analogue of IV methods). Achieved via DP priors. peter r ................................ Peter E. Rossi Joseph T. and Bernice S. Lewis Professor of