similar to: dev.copy(postscript,...) generates a disrupted string

Hi there, I have a multiple for loop over a list of data frames for ( i in 1:(N-1) ) { for ( j in (i+1):N ) { for ( p in 1:M ) { v_i[p] = alist[[p]][i,"v"] v_j[p] = alist[[p]][j,"v"] } rho_s = cor(v_i, v_j, method = "spearman") rho_p = cor(v_i, v_j, method = "pearson"

Hey guys, sorry for the inconvenience (this might be a hundred times answered question), but I have been searching a while and gave up about the following: I have the following, table and data: table <- seq(255, 0, by=-1) data <- c(1,8,...) <--- doesn't matter what's in here Which would be the most efficient way to replace each data value, v_i, by table[v_i + 1] ? And, maybe

I am trying to use lme to fit a mixed effects model to get the same results as when using the following SAS code: proc mixed; class refseqid probeid probeno end; model expression=end logpgc / ddfm=satterth; random probeno probeid / subject=refseqid type=cs; lsmeans end / diff cl; run; There are 3 genes (refseqid) which is the large grouping factor, with 2 probeids nested within each refseqid,

Hello all, Is there a way in R to compute the multivariate normal density of every pair of entries in a vector efficiently instead of using for loop? For example Suppose I have a vector a=c(v_1,...,v_p)=c(0.5343909, -0.7784353, -0.0568370, 1.8772838, -1.3183407, 0.8227418,...) I want to compute density(v_i, v_j) for every pair of entries (i,j) (i!=j) in a. The joint bivariate distribution

Hi. If I have a vector, v_1, and another vector of positive integers, i_1, the same length as v_1, then rep(v_1,i_1) will repeat v_i[j] exactly i_1[j] times, like so: >rep(c(1,2,3),c(3,2,1)) [1] 1 1 1 2 2 3 > I'd like to do the same sort of thing where I replace v_1 with a matrix, and the jth row of the matrix is repeated i_1 times. Obviously, I could do this with for loops, like

I am trying to use t.test on the following data: date type INTERVAL nCASES MTF SDF MTO SDO nFST MF nOBS MO MB BIASCV BIASEV ME MAE RMSE CRCF 2001-06-15 avn GE1.00 4385 0.246 0.300 1.502 0.556 1367 1.373 4385 1.502 1.471 0.285 0.164 -1.256 1.266 1.399 0.056 2001-06-15 avn

Dear All, Thank you for the replies to my first thread here: http://r.789695.n4.nabble.com/global-optimisation-with-inequality-constraints-td3799258.html. So far the best result is achieved via a penalised objective function. This was suggested by someone on this list privately. I am still looking into some of the options mentioned in the original thread, but I have been advised that there may

Hello. Slope estimates in lme are shrinkage estimates which pull the OLS slope estimates towards the population estimates, the degree of which depends on the group sample size and the distance between the group-based estimate and the overall population estimate. Although these shrinkage estimates as said to be more precise with respect to the true values, they are also biased. So there is a

https://bugs.freedesktop.org/show_bug.cgi?id=93630 Bug ID: 93630 Summary: [NVE6] disrupted display, cannot switch VT, everything else still works, E[ PDISP] link training failed Product: xorg Version: unspecified Hardware: Other OS: All Status: NEW Severity: normal

I am trying to use lme to fit a mixed effects model to get the same results as when using the following SAS code: proc mixed; class refseqid probeid probeno end; model expression=end logpgc / ddfm=satterth; random probeno probeid / subject=refseqid type=cs; lsmeans end / diff cl; run; There are 3 genes (refseqid) which is the large grouping factor, with 2 probeids nested within each refseqid,

R Experts: I am conducting a meta-analysis where the effect measures to be pooled are simple proportions. For example, consider this data from Fleiss/Levin/Paik's Statistical methods for rates and proportions (2003, p189) on smokers: Study N Event P(Event) 1 86 83 0.965 2 93 90 0.968 3 136 129 0.949 4 82 70 0.854 Total

Ei-ji Nakama has pointed out (from another Japanese user, I believe) that postscript() and pdf() have not been handling kerning correctly, and this is a request for opinions about how we should correct it. Kerning is the adjustment of the spacing between letters from their natural width, so that for example 'Yo' is usually typeset with the o closer to the Y than 'Yl' would be.

I buy flowers at a local market on a fairly regular basis. The flower vendors post their prices and if I want to buy only one or two flowers I will generally get the posted price. From time to time I want to buy large quantities of flowers, and sometimes a vendor will give me a better price than their posted price for the bulk order, but more often I have to offer them a higher price than the

We use Asterisk 14 to proxy calls between two servers, 10.0.0.192 to 10.0.0.228. But sometimes another of our servers becomes listed as a SIP agent, even though the server's IP address isn't part of our sip.conf, extensions.conf, nor any other config I know of. For example in the log snippet below, the source server experienced an SDP renegotiation in the middle of a call, and seemingly as

Dear R users, I'm trying to do betareg on my dataset. Dependent variable is not normally distributed and is proportion (of condom use (0,1)). But I'm having problems: gyl<-betareg(cond ~ alcoh + drug, data=results) Error in optim(par = start, fn = loglikfun, gr = gradfun, method = method, : initial value in 'vmmin' is not finite Why is R returning me error in optim()? What

On Thu, 12 Apr 2007, Fernando Magno Quintao Pereira wrote: >> I'm definitely interested in improving coalescing and it sounds like >> this would fall under that work. Do you have references to papers >> that talk about the various algorithms? > > Some suggestions: > > @InProceedings{Budimlic02, > AUTHOR = {Zoran Budimlic and Keith D. Cooper and Timothy

Hello, and please excuse this off-topic question, but I have not been able to find an answer elsewhere. Consider a value Z that is calculated using the product (or ratio) of two means X_mean and Y_mean: Z=X_mean*Y_mean. More generally, Z=f(X_mean, Y_mean). The standard error of Z will be a function of the standard errors of the means of X and Y. I want to calculate this se of Z. Can someone

A question about: Kalman in R, time series and deJong-Penzer statistic - how to compute it using available artefacts of KalmanXXXXX? Background. in the paper http://www.lse.ac.uk/collections/statistics/documents/researchreport34.pdf 'Diagnosing Shocks in TIme Series', de Jong and Penzer construct a statistic (tau) which can be used to locate potential shocks. [p15, Theorem 6.1 and

For a long string in an axis title, or main title the postscript device breaks apart the long strings into smaller strings. For example, > postscript('linebreaktest.eps') > plot(1,xlab='aReallyLongStringToSeeHowItBreaks',ylab='aReallyLongStringToSeeHowItBreaks') > for(i in c(.6,1,1.4))text(i,i,'aReallyLongStringToSeeHowItBreaks') > dev.off() produces

In a classical meta analysis model y_i = X_i * beta_i + e_i, data {y_i} are assumed to be independent effect sizes. However, I'm encountering the following two scenarios: (1) Each source has multiple effect sizes, thus {y_i} are not fully independent with each other. (2) Each source has multiple effect sizes, each of the effect size from a source can be categorized as one of a factor levels