similar to: geeglm crashes if there are no datapoints in predictor's first level (PR#13266)

Hi there. I have the following graph: http://www.nabble.com/file/p10928148/map.jpg However, some datapoints occur at the same place as other datapoints and are so layered on top of each other. I would like to know if there is any possible way in which I could view those datapoints that are layered on top of each other ...maybe by rotating using latitude to show the datapoints underneath (but

https://bugzilla.samba.org/show_bug.cgi?id=13266 Bug ID: 13266 Summary: Writing of files to Apple's new "AP" controller model SSD's takes 4x longer. Product: rsync Version: 3.1.3 Hardware: x64 OS: Mac OS X Status: NEW Severity: critical Priority: P5

Dear Ms and Mr R, I am seeking a visually appealing presentation of 3 variables (~30 datapoints) that are correlated and that should to into the same direction. The objective is to show that they do go in the same direction and what differences there might be while also giving an idea of the values of the variables. I had done this with simple bargraphs for 6 datapoints in the past, but for

Thanks, Martin. Based on my previous post, I thought of a more general formulation of my question that I think would be helpful to ask here. What's the best way to build an R object that links multiple datapoints about different people? I mean, I happen to have datasets that have individual gene expression data tied to individual patient characteristics (how long they survived, age, gender,

It is known that function bkde2D (package "KernSmooth") returns a matrix of density estimates over the mesh induced by x1 and x2. In Details it is written that "... heights of the kernel, scaled by the bandwidths, at each datapoint are summed. This sum, after a normalization, is the corresponding fhat value in the output". There are several questions: 1) How to calculate

Hello!!! I'm just a beginner of R. I work with locfit to get the density from an numeric vector. It looks good when I plot the graph, but I need the output data of locfit. For example I need somthing like that (only integer values for the input): numeric vector (input of locfit) density (output of locfit) 1 0.05.... 2 0.04.. 3 0.01 ... Thank you for your help and your time.

Hi, In the getGraphicsEvent function the (x,y) co-ordinates returned from the mouse move are in relation to where the mouse is located within the device window (i.e. the lower left corner of the window is '(0,0)', the upper right is '(1,1)'). Is there a way of returning the (x,y) co-ordinates of data points plotted where instead of x and y being the actual data points they are

Hello!! At first thank you all for your suggestions and your help. But I would like to get another group of datas from the density estimation. I don't know if these information are provided by locfit or I should use an other function like plot(). Description: Each number between 1 and 100 (only integer) for example should have only one density value. But I also would like to have the density

Hi all, I have a time series like that : Date Value 01/03/05 -0.008471364 01/04/05 -0.008153802 01/05/05 -0.000780031 01/06/05 -0.000130064 01/07/05 -0.000650576 01/08/05 -0.000130166 01/10/05 -0.004174282 01/11/05 0.01027384 01/12/05 -0.006099558 01/13/05 -0.009811054 01/14/05 0.000657073 I import this dataset as a zoo object. Now I want to fetch the

On Feb 2, 2014, at 6:18 PM, Andrew Trick <atrick at apple.com> wrote: >> The result of such a system would produce weights for every block in the above CFG as '1.0', or equivalent to the entry block weight. This to me is a really useful metric -- it indicates that no block in the CFG is really more or less likely than any other. Only *biases* in a specific direction would

Hello, I'm implementing a custom Haskell-to-LLVM compiler, and in my experimentation, noticed that GHC is much slower than clang certain examples, such as the ackermann function. However, from reading their respective IRs (Cmm for GHC and LLVM for clang), I don't really see much of a difference. Here is a link to the numbers. (n, m) are the parameters to the ackermann function

Dear list, this is perhaps more of a statistics question than an R question, but perhaps someone could help me out anyway. I'm doing sociological research and am currently in the process of familiarizing myself with the basic concepts of multiple imputation. Eventually, my goal is to perform quantile regression on a large data set, where one non-negative discrete variable contains missing

A student brought this question to me and I can't find any articles or examples that are directly on point. Suppose there are 2 ordinal logistic regression models, and one wants to set them into a simultaneous equation framework. Y1 might be a 4 category scale about how much the respondent likes the American Flag and Y2 might be how much the respondent likes the Republican Party in America.

Hello all, I want to assign relative score to the predictor variables on the basis of its influence on the dependent variable. But I could not find any standard statistical approach appropriate for this purpose. Please suggest the possible approaches. Thanks in advance, Rupendra Chulyadyo Institute of Engineering, Tribhuvan University, Nepal [[alternative HTML version deleted]]

Hi, I'm new to R and using it for Cox survival analysis. Thanks to this great forum I learned how to compute the HR with its confidence interval. My question would be: Is there any way to get the p-value for a hazard ratio in addition to the confidence interval? Thanks, Thierry -- Thierry Panje Visiting Student Researcher Department of Psychology Stanford

Right now, all profile information is funneled through two analysis passes prior to any part of the optimizer using it. First, we have BranchProbabilityInfo, which provides a simple interface to the simplest form of profile information: local and relative branch probabilities. These merely express the likelihood of taking one of a mutually exclusive set of exit paths from a basic block. They are