similar to: covariance

A handful of files in compiler-rt/builtins use the macro "__SOFT_FP__". All the uses are along the same lines. Here's a representative example from compiler-rt/lib/builtins/fixdfdi.c: #ifndef __SOFT_FP__ // Support for systems that have hardware floating-point; can set the invalid // flag as a side-effect of computation. ... #else // Support for systems that don't have

I think I should have posted this question here as well. I am posting my question here since it is R related. Please see below. I originally posted this to sci.stat.math "Nasser Abbasi" <nma at 12000.org> wrote in message news:Mm4gj.28843$R92.4987 at newsfe16.phx... > > I think R documentation is a bit hard for me to sort out at this time. > > I was wondering if

hi; When I first started looking at R code, I thought that the <- notation for assignment made the code less readable (and I still do). Then I found that now one can use "=" in place of "<-" for assignment (I understand this started since version 1.4). Anyway, I think using "=" makes the code much more readable. I was wondering, would it be safe if I

Dear R-community, Using bwplot, how can I put the whiskers at percentile 5 and percentile 95, in place of the default position coef=1.5?? Using panel=panel.bwstrip, whiskerpos=0.05, from the package agsemisc gives satisfaction, but changes the appearance of my boxplot and works with an old version of R, what I don’t want, and I didn’t find the option in box.umbrella parameters Many thanks

Specifying -no-implicit-float prevents LLVM from using non-GPR registers for purely integer operations. This is useful for operating systems (such as Wind River's VxWorks) that support tasks that do not save all registers on context switch. This presents an interesting problem for variadic functions that may optionally take non-integer arguments (e.g. printf style functions). Should non-GPR

Hello everyone. I would like to find the sample covariance matrix using R. So far I read on the wikipedia what a sample_covariance is http://en.wikipedia.org/wiki/Sample_covariance according to wikipedia one vector is enough to calculate the sample covariance matrix. In R I tried cov(myvector) and I get the reply that I need to pass either two argument or one matrix with x,y values . How can I

Hi, I have a set of covariance matrices but not the original data. I want to carry out some exploratory factor analysis. So, I am trying to construct a covariance matrix list as the input for factanal. I can construct a list which includes the cov, the centers, and the n.obs. But it doesn't work. I get an error that says "Error in sqrt(diag(cv)) : Non-numeric argument to mathematical

Hi, Excuse me for asking this silly question. But I really couldn't understand why cov() and ccov() don't work for my calculation of covariance matrix. a <- matrix(1:8, 2, 4) a [,1] [,2] [,3] [,4] [1,] 1 3 5 7 [2,] 2 4 6 8 > ccov(a) Error in solve.default(cov, ...) : Lapack routine dgesv: system is exactly singular I also tried colume bind, but it

Anybody knows what functions can be used to calculate variance/covariance with complex numbers? var and cov don't seem to work: > a 1 V1 0.00810014+0.00169366i V2 0.00813054+0.00158251i V3 0.00805489+0.00163295i V4 0.00809141+0.00159533i V5 0.00813976+0.00161850i > var(a) 1 1 1.141556e-09 Warning message: In var(a) : imaginary parts discarded in

Dear list, I am trying to use the 'mvrnorm' function from the MASS package for simulating multivariate Gaussian data with given covariance matrix. The diagonal elements of my covariance matrix should be the same, i.e., all variables have the same marginal variance. Also all correlations between all pair of variables should be identical, but could be any value in [-1,1]. The problem I am

I would appreciate confirmation that the function vcov(model.nlme) gives the var-cov matrix of the fixed effects in an nlme model. Presumably the random-effects var-cov matrix is given by cov(ranef (model.nlme)? Rob Forsyth

Hello, I have a vector {a, b1, b2, b3, b4}. How can I calculate the following matrix: var(a) cov(a, b1) cov(a, b2) cov(a, b3) cov(a, b4) cov(a, b1) var(b1) cov(a, b2) cov(a, b3) cov(a, b4) ... ... cov(a, b1) cov(a, b2) cov(a, b3) cov(a, b4) var(b4) I would very appreciate your inputs. Thank you very much. Sincerely, Jialin Huang [[alternative HTML version deleted]]

Hello everyone., I am looking for a covariance function this not the first time I have this type of problem (to find which function does something). I try in google with "R cran covariance function" but usually this ends with different results that do not help me that much. Could you please try to advice me how to search for what function implements the functionality you want.

Hi, I am running a simple linear model with (say) 5 independent variables. Is there a simple way of getting the variance-covariance matrix of the coeffcient estimates? None of the values of the lm() seem to provide this. Thanks in advance, Ritwik Sinha rsinha@darwin.cwru.edu Grad Student Case Western Reserve University [[alternative HTML version deleted]]

Dear list I need your help: Execuse me for my limited R knowledge. #example data set set.seed (134) lm=c(1:4) block = c(rep(lm,6)) gen <- c(rep(1, 4), rep(2, 4), rep(3, 4), rep(4, 4),rep(5, 4),rep(6, 4)) X1 = c( rnorm (4, 10, 4), rnorm (4, 12, 6), rnorm (4, 10, 7),rnorm (4, 5, 2), rnorm (4, 8, 4), rnorm (4,7, 2)) X2 = X1 + rnorm(length(X1), 0,3) yvar <- c(X1, X2) X <- c(rep( 1,

Hi all, I have a matrix that has many NaN values. As soon as one of the columns has a missing (NaN) value the covariance estimation gets thrown off. Is there a robust way to do this? Thanks, Sachin a=array(rnorm(9),dim=c(3,3))> a [,1] [,2] [,3] [1,] -0.79418236 0.7813952 0.855881 [2,] -1.65347906 -1.9462446 -0.376325 [3,] -0.03144987 0.6756862 -1.879801>

Hi, I want to simulate a data set with similar covariance structure as my observed data, and have calculated a covariance matrix (dimensions 8368*8368). So far I've tried two approaches to simulating data: rmvnorm from the mvtnorm package, and by using the Cholesky decomposition (http://www.cerebralmastication.com/2010/09/cholesk-post-on-correlated-random-normal-generation/). The problem is

Dear all, Does this make any sense: var() = cov() != acf(lag.max=0, type="covariance")? I have daily data of IBM for May 2005, and I'm using the logarithmic return: > ibm200505$LRAdj.Close [1] NA 0.0203152 0.0005508 -0.0148397 -0.0025182 0.0092025 -0.0013889 [8] 0.0098196 -0.0103757 -0.0274917 0.0005716 -0.0159842 -0.0074306 0.0091710 [15] 0.0002898 0.0226306

Hello, Sorry. It may be a stupid question. I have two vectors a<-c(9,3,5) b<-c(3,4,1) How can I get the variance-covariance matrix of these two vectors? I tried cov(a,b), I got a number not a matrix. I tried to transpose vector a and b as t(a) and t(b), it still cannot work. Any suggestions? Thank a lot! -- View this message in context:

Hi. It's my understanding that a cross-correlation function of vectors x and y at lag zero is equivalent to their correlation (or covariance, depending on how the ccf is defined). If this is true, could somebody please explain why I get an inconsistent result between cov() and ccf(type = "covariance"), but a consistent result between cor() and ccf(type = "correlation")? Or