search for: nsimulations

Dear list, I would like to rename an object as follows: SimLUall <- matrix(c(0,1,0,0, 1,0,0,0, 0,0,1,0, 0,0,0,1),nrow=4, ncol=4) j <- 2 SimLUall2 <- SimLUall and j The value of j should be assigned automatically to SimLUall. How can I achieve this? Regards, Ulrich

Hi R Helpers, I'm still new to R and i experience many difficulties..I'm using vegan package (R version 2.11) trying to calculate checkerboard units for each species pair of a matrix. I've prepared the function: pair.checker=function (dataset) {designdist (dataset, method="c("(A-J)x(B-J)", terms ="binary", abcd=FALSE)} to use with function oecosimu as

This is my current work.Now i am trying to use a function to do the normal distribution simulation. rm(list=ls()) t <- u<- mann<- rep(0, 45) Nsimulation<-function(S1,S2,Sds,nSims) { set.seed(1) for (sim in 1:nSims) { matrix_t <-matrix(0,nrow=nSims,ncol=3) matrix_u<-matrix(0,nrow=nSims,ncol=3)

Dear folks, I have been struggling to create what I fondly imagined would be a straightforward adaptation of the package's example to my own dataset, which looks at incidence of depression following the birth of initial and up to 3 subsequent children (4 children in all, with all subjects having a first child). I've regarded these as equivalent to tooth eruption and subsequent caries in

...ariables within lists in R. I am running simulations and am interested in two parameters, ESM and ESMM (the similarity of these names is important for my question). I do simulations to generate ESMM, then plug these values into a second simulation function to get ESM: x <- list() for (i in 1:nsimulations) { x$ESMM[i] <- do_simulation1() x$ESM[i] <- do_simulation2(x$ESMM[i]) } and I return everything as a dataframe, x <- as.data.frame(x) When I do this, I find that x$ESMM is overwritten by x$ESM for the first simulation. However, x$ESM is nonetheless correctly generated using x$ESMM....

HI, I am trying to use switch () function to connect the three distribution (normal ,gamma with equal skewness and gamma with unequal skewness. But i am losing my ideas since i have sample sizes-(10,10),(10,25),(25,25),(25,50),(25,100),50,25),(50,100), (100,25),(100,100) standard deviation ratio- (1.00, 1.50, 2.00, 2.50, 3.00 and 3.50) distribution of gamma distribution with unequal skewness

...Here are some issues I see: 1) The much simpler method of using the consecutive integers as seeds also seemed to work. This also has the advantage of repeatability. I avoided it because I was concerned it wouldn't be random enough. Would consecutive integers as in parLapply(cluster, seq(nSimulations), function(i) myfunction(seed=i)) be sufficient? I suppose I could also generate all the random seeds on the master. 2) This got me thinking about how to generate random integers that span the whole range of 32 bit signed integers. The method show above only spans half the range, since .Machine$...