similar to: the function doesn´t work

hey, how can i plot this function??? thanks for ur help n=1000 m=2 k=n/m N=100 myfun <- function(n, m, alpha = .05, seeder = 1000) { l=matrix(0,nrow=m,ncol=N) for(i in 1:N){ set.seed(i) for(j in 1:m){ x=rnorm(n,0,0.5) y=rnorm(n,0,0.8) l[j,i]=cor((x[(((j-1)*k)+1):(((j-1)*k)+k)]), (y[(((j-1)*k)+1):(((j-1)*k)+k)])) } } for(i in 1:N){ for (j in 1:m){ gute <- function() { q_1 <-

hi how can i plot now this function??? have to be m= 2??? because of the dimensions?thanks for ur help myfun <- function(n, m, alpha = .05, seeder = 1000) { set.seed(seeder) x <- matrix(rnorm(n, 0, 0.5), ncol = m) y <- matrix(rnorm(n, 0, 0.8), ncol = m) l <- diag(cor(x, y)) cat("Correlations between two random variables \n", l, fill = TRUE) gute

I have been playing around with sparse matrices in the Matrix package, in particularly with the Cholesky factorization of matrices of class dsCMatrix. And BTW, what a fantastic package. My problem is that I have to carry out repeated Cholesky factorization of a spares symmetric matrices, say Q_1, Q_2, ...,Q_n, where the Q's have the same non-zero pattern. I know in this case one does

Hello, i wrote this function guete and now i want to plot it: but i get this error message. i hope someone can help me. Error in dim(robj) <- c(dX, dY) : dims [product 16] do not match the length of object [1] p_11=seq(0,0.3,0.1) p_12=seq(0.1,0.4,0.1) guete = function(p_11,p_12) { set.seed(1000) S_vek=matrix(0,nrow=N,ncol=1) for(i in 1:N) { X_0=rmultinom(q-1,size=1,prob=p_0)

On 07/29/2013 09:15 AM, Sven Verdoolaege wrote: > On Mon, Jul 29, 2013 at 07:37:14AM -0700, Tobias Grosser wrote: >> On 07/29/2013 03:18 AM, Sven Verdoolaege wrote: >>> On Sun, Jul 28, 2013 at 04:42:25PM -0700, Tobias Grosser wrote: >>>> Sven: In terms of making the behaviour of isl easier to understand, >>>> it may make sense to fail/assert in case

Hi Sebastian, Recently, I found the "Polly - Calculate dependences" pass would lead to significant compile-time overhead when compiling some loop-intensive source code. Tobias told me you found similar problem as follows: http://llvm.org/bugs/show_bug.cgi?id=14240 My evaluation shows that "Polly - Calculate dependences" pass consumes 96.4% of total compile-time overhead

I have a data frame in wide format. There are six variables that represent two factors in long format 3x2, Valence and Temperature: > head(dpts) File Subj Time Group PainNeg.hot PainNeg.warm SociNeg.hot SociNeg.warm Positiv.hot Positiv.warm Errors 1 WB101_1_1_dp.txt 101 1 MNP 30.700000 13.75000 16.319048 35.166667 30.18333 14.383333 1 2

Dear Tobias, Thank you very much for your very helpful advice. Yes, -debug-pass and -time-passes are two very useful and powerful options when evaluating the compile-time of each compiler pass. They are exactly what I need! With these options, I can step into details of the compile-time overhead of each pass. I have finished some preliminary testing based on two randomly selected files from

On 04/30/2013 04:13 PM, Star Tan wrote: > Hi all, [...] > How could I find out where the time is spent on between two adjacent Polly passes? Can anyone give me some advice? Hi Star Tan, I propose to do the performance analysis using the 'opt' tool and optimizing LLVM-IR, instead of running it from within clang. For the 'opt' tool there are two commands that should help

On 05/03/2013 11:39 AM, Star Tan wrote: > Dear Tobias, > > > Thank you very much for your very helpful advice. > > > Yes, -debug-pass and -time-passes are two very useful and powerful > options when evaluating the compile-time of each compiler pass. They > are exactly what I need! With these options, I can step into details > of the compile-time overhead of each pass.

Sorry to all that are angry about the form of my previous mail. I didn't realise what would happen :((. Here it is in (hopefully) plain text (if my mailer doesn't spoil it again): ############## Dear developers, I have a problem with some discrepancy between R 1.2.1 for Windows and R 1.2.2 (and less) for Linux. While trying to correct the wilcox.test (see my previous bug report) I

Hello, Well, try it: p <- .Machine$double.eps^seq(0.5, 1, by = 0.05) z <- qnorm(p/2) pnorm(z) # [1] 7.450581e-09 1.228888e-09 2.026908e-10 3.343152e-11 5.514145e-12 # [6] 9.094947e-13 1.500107e-13 2.474254e-14 4.080996e-15 6.731134e-16 #[11] 1.110223e-16 p/2 # [1] 7.450581e-09 1.228888e-09 2.026908e-10 3.343152e-11 5.514145e-12 # [6] 9.094947e-13 1.500107e-13 2.474254e-14 4.080996e-15

I would like to generate pseudo-random numbers between two numbers using R, up to a given distribution, for instance, rnorm. That is something like rnorm(HowMany,Min,Max,mean,sd) over rnorm(HowMany,mean,sd). I am wondering if dnorm(runif(HowMany, Min, Max), mean, sd) is good. Any idea? Thanks. -james

You may want to look into using the log option to qnorm e.g., in round figures: > log(1e-300) [1] -690.7755 > qnorm(-691, log=TRUE) [1] -37.05315 > exp(37^2/2) [1] 1.881797e+297 > exp(-37^2/2) [1] 5.314068e-298 Notice that floating point representation cuts out at 1e+/-308 or so. If you want to go outside that range, you may need explicit manipulation of the log values. qnorm()

Dear all, I have the following problem with the uniroot function. I want to find roots for the fucntion "Fp2" which is defined as below. Fz <- function(z){0.8*pnorm(z)+p1*pnorm(z-u1)+(0.2-p1)*pnorm(z-u2)} Fp <- function(t){(1-Fz(abs(qnorm(1-(t/2)))))+(Fz(-abs(qnorm(1-(t/2)))))} Fp2 <- function(t) {Fp(t)-0.8*t/alpha} th <- uniroot(Fp2, lower =0, upper =1,

Hi, I am new in R and stumbled on a problem my (more experienced) friends can not help with with. Why isnt this code working? The function is working, also with the loop and the graph appears, only when I build another loop around it (for different values of p) , R stays in a loop? Can't it take more then 2 loops in one program? powerb<-function(x,sp2,a,b,b1,m) {

Hi, im little bit confused about Cohen's Kappa and i should be look into the Kappa function code. Is the easy formula really wrong? kappa=agreement-chance/(1-chance) many thanks christian ############################################################################### true-negativ:7445 false-positive:3410 false-negativ:347 true-positiv:772 classification-aggrement:68,6%

Greetings! I suspect that there is an error in the code for the function ci.pd() in the Epi package. This function is for computing confidence intervals for a difference of proportions between two independent groups of 0/1 responses, and implements the Newcombe ("Nc") method and the Agrasti-Caffo "AC" method. I think there is an error in the computation for the Newcombe

I'm trying to convert an S-Plus program to R.  Since I'm a SAS programmer I'm not facile is either S-Plus or R, so I need some help.  All I did was convert the underscores in S-Plus to the assignment operator <-.  Here are the first few lines of the S-Plus file:   sshc _ function(rc, nc, d, method, alpha=0.05, power=0.8,              tol=0.01, tol1=.0001, tol2=.005, cc=c(.1,2),

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