similar to: inquire a statistical terms

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

Dear R users, In my data, there are two variables t1 and t2. For each observation of t1 and t2, two location indicators (x, y) were provided. The data format is # x y t1 t2 Since the both t1 and t2 are depended on x and y, t1 and t2 are autocorrelated variables. My question is how to calculate the correlation between t1 and t2 by taking into account the structure of residual variance

Dear Friends, Let's assume there are three parameters that were passed into fun1. In fun1, we need to modify one para but the remains need to be untouched. And then all parameters were passed into fun2. However, I have failed to achieve it. Please see the following code. ########################################## fun1 <-function(x, y, z=10) {x+y+z;} fun2 <-function(aa, ...) {

I have distribution that are gaussian to a good approximation. I fit a gaussian to these distributons. Once in a while there is an outlier. Could someone suggest a robust method (R package already?) that removes those outliers and redoes the gaussian fit to get a better fit? Thanks. [[alternative HTML version deleted]]

I have a statistical question. The data sets I am working with are right-skewed so I have been plotting the log transformations of my data. I am using a Grubbs Test to detect outliers in the data, but I get different outcomes depending on whether I run the test on the original data or the log(data). Here is one of the problematic sets: fgf2p50=c(1.563,2.161,2.529,2.726,2.442,5.047)

Hmm. Chuck: I don't see how this example represents incomplete/incommensurate recycling. Doesn't TRUE replicate from length-1 to length-3 in this case (mat[c(TRUE,FALSE),2] would be an example of incomplete recycling)? William: clever, but maybe too clever unless you really need the speed? (The clever way is 8 times faster in the following case ...) x <- rep(1,1e6)

Sorry if this has been covered here somewhere in the past, but ... Does anyone know why logical vectors are *silently* recycled, even when they are incommensurate lengths, when doing logical indexing? This is as documented: For ?[?-indexing only: ?i?, ?j?, ?...? can be logical vectors, indicating elements/slices to select. Such vectors are recycled if necessary to match

Hi Folks, I'm trying to find a way to remove all terms in a formula that contain a particular interaction. For example, in the formula below, I'd like to remove all terms that contain the b:c interaction. > attributes(terms( ~ a*b*c*d))$term.labels [1] "a" "b" "c" "d" "a:b" "a:c" [7]

Dear all, How can i obtain odds ratio in logistic regression when the model contains interaction terms in R? how can i obtain OR for a special case? Thanks in advance for any help. Amin

Hello, I'm analysing reaction time data from a linguistic experiment (a variant of a lexical decision task). To ascertain that the data was normally distributed, I used *shapiro.test *for each participant (see commands below), but only one out of 21 returns a p value above p.0 05. > f = function(dfr) return(shapiro.test(dfr$Target.RTinv)$p.value) > p = as.vector(by(newdat,

Hi Nadav, On Thu, Jan 3, 2013 at 1:53 PM, Nadav Rotem <nrotem at apple.com> wrote: > Hi Akira! > > > > > Does the current loop vectorizer inquire about the SIMD capabilities of > the target architecture when it decides whether it is profitable to > vectorize a loop? > > Yes, it uses a cost model to determine the profitability of vectorization. > At the

Hi Akira! > > Does the current loop vectorizer inquire about the SIMD capabilities of the target architecture when it decides whether it is profitable to vectorize a loop? Yes, it uses a cost model to determine the profitability of vectorization. At the moment only x86 provides the necessary hooks that are needed for calculating the costs. We may need to change the cost defaults to

Nadav (or anyone who is familiar with the loop vectorizer), Does the current loop vectorizer inquire about the SIMD capabilities of the target architecture when it decides whether it is profitable to vectorize a loop? I am asking this because I would like to have loop vectorization disabled for targets that don't support SIMD instructions (for example, standard mips32). Loop vectorization

On 3 January 2013 21:53, Nadav Rotem <nrotem at apple.com> wrote: > > I am asking this because I would like to have loop vectorization > disabled for targets that don't support SIMD instructions (for example, > standard mips32). > > Loop vectorization bloats the code size and prolongs compilation time > without any improvement to performance for such targets. >

On Jan 3, 2013, at 2:05 PM, Renato Golin <renato.golin at linaro.org> wrote: > Isn't the vectorizer disabled by default? The loop vectorizer is now enabled by default. -------------- next part -------------- An HTML attachment was scrubbed... URL: <http://lists.llvm.org/pipermail/llvm-dev/attachments/20130103/4645b2e8/attachment.html>

On Jan 3, 2013, at 3:04 PM, Renato Golin <renato.golin at linaro.org> wrote: > On 3 January 2013 22:09, Nadav Rotem <nrotem at apple.com> wrote: > The loop vectorizer is now enabled by default. > > I thought that was just a temporary arrangement to get the feel for it, not to actually have it on all the time (next release). Is it just for -O3 or lower too? The plan it

Is there a way to ask asterisk from a shell script if its connection (SIP) is valid to another system. Lets say for example to cisco call manager? Thanks, Jerry

I am looking for the binary rw1051. I have some code that will only run in version 1.5.1 and I need to install the old binary code. Is there a repository for previous R versions? Regards, Ernesto Calvo [[alternative HTML version deleted]]

Hi all, I've fit a mixed linear model to some longitudinal data. I'm interested in the differences in patterns of decrease in the dependent variable according to group status, and my hypothesis particularly predicts a difference between the groups in trajectory of change at between specific ages. The data shows a significant interaction between group and the linear and quadratic effects

Hi all, I found that the PCA gave chaotic results when there are big changes in a few data points. Are there "improved" versions of PCA in R that can help with this problem? Please give me some pointers... Thank you! [[alternative HTML version deleted]]