similar to: How to create a random matrix with conditions

Hi all. I was having some trouble with a for loop and I found the problem is the following. Does anyone have some idea why I got the following R result? Since mone is equal to 3, why mu1 only have 2 components? library(MASS) > p0 <- seq(0.1, 0.9,by=0.1) > m <- 10 > > > p0 <- p0[7] > > ## data generation > > mzero <- p0*m > mone <- m-mzero >

Dear R users, I am running simulations (1000), and in my simulation I am looking at specific sums. For example, if the sum is >=4 then count this, if say <3, then don't count, if the sum=3, then generate a random number from uniform distribution, if this number is say less than 0.5, then count this sum, if greater than 0.5, then don't count. I am having trouble with introducing this

Hello. Somebody knows how can I generate a set of n random vectors of a normal distribution of several variables? For example, I want to generate n=100 random vectors of two dimensions for a normal with mean c(0,1) and variance matrix: matrix(c(2,1,1,3),2,2). Thanks in advance, Arnau.

Hi all, I have two dataframes. The first (A) contains all the stock prices for today including today. So the first column is the stock Symbol and the second column is the stock price. The second (B) is the symbol list in the top 100 stocks. I want to pick out from dataframe A only the rows containing the symbols from B. i.e. something like: prices <- A[A[,1]==B,2] is there any

Hi all, [this is a bit hard to describe, so if my initial description is confusing, please try running my code below] #WHAT I'M TRYING TO DO I'd appreciate any help in trying to speed up some code. I've written a script that converts a matrix of integers (usually between 1-10,000 - these represent allele names) into two new matrices of normally distributed values (representing

On 3/11/2018 3:35 PM, Christofer Bogaso wrote: > But for my reporting purpose, I need to generate a bell curve like > plot based on empirical PDF, that also contains original points. > > Any idea would be helpful. Thanks, > Christofer, something like the following may get you what you want: ## get the kernel density estimate dens <- density(Dat) ## estimate the density at

Dear list, I tried to find a solution for this problem from the archives but couldn't find any. I would like sample sequence of ten days from vector d d <- seq(as.Date("2007-02-12"), as.Date("2008-08-18"), by="days") so that the days follow each other (sample(d, 10) is not the appropriate solution). Any ideas? Best regards, Lauri

But for my reporting purpose, I need to generate a bell curve like plot based on empirical PDF, that also contains original points. Any idea would be helpful. Thanks, On Mon, Mar 12, 2018 at 3:49 AM, Bert Gunter <bgunter.4567 at gmail.com> wrote: > You need to re-read ?density and perhaps think again -- or do some study -- > about how a (kernel) density estimate works. The points at

I tried to use gpuCor function in the gputools package of R to calcuate the pairwise correlations of a matrix of 40,000 columns. Becuase there would be memory issues if I use the whole matrix at a time, I splitted the matrix into submatrix of 10,000 columns and then calculate the pairwise correlation of different submatrices. There are altogether 4 submatrices, so I need to calculate the pearson

Hello r world, Does anyone know a function or package that can compute correlations between sets of XY coordinates? Thanks in advance for your help, Chris

I want to generate multiple multivariate normal samples with different mean vectors and common covariance matrix. I can do this with a loop, but can't quite figure out how to do it with apply and friends. In the example below, I want values to have 3 columns: group, x, y # number of groups, and group means x <- jitter(seq(2,10,by=2)) y <- x + rnorm(length(x), 0, .5) means <-

Dear R helpers I have a data frame as given below df = data.frame(A = c(776,827,836,995,855,1026,1203,1363,965,1195),                        B = c(806,953,1049,1056,1243,764,1148,1162,948,1154),                        C = c(959,1155,1193,1163,863,1070,1087,877,1132,944),                        D = c(906,760,978,1170,1009,883,1007,960,828,113)) # Actually the real data has number of vectors and

Hi all, I have a data set (df, n=10 for the sake of simplicity here) where I have two continuous variables (age and weight) and I also have a grouping variable (group, with two levels). I want to run correlations for each group separately (kind of similar to "split file" in SPSS). I've been experimenting with different functions, and I was able to do this correctly using ddply

Are they the same with .RData being the newer format?? Thanks, ...Tao

Dear friends, regression towards the mean is interesting in medical circles, and a very recent paper (The American Statistician November 2007;61:302-307 by Krause and Pinheiro) treats it at length. An initial example specifies (p 303): "Consider the following example: we draw 100 samples from a bivariate Normal distribution with X0~N(0,1), X1~N(0,1) and cov(X0,X1)=0.7, We then calculate

I need to create artificial datasets with specific correlation coefficients (i.e. a dataset that returns r = 0.30, etc.) as examples for a lab I am teaching this summer. Is there a way to do that in R? Thanks. Jim Milks Graduate Student Environmental Sciences Ph.D. Program 136 Biological Sciences Wright State University 3640 Colonel Glenn Hwy Dayton, OH 45435 [[alternative HTML version

Dear all, I have an R script that uses Rcpp, and I have been trying to parallelize it using mclapply (I tried with the multicore and the parallel library) Sometimes (not always, interestingly), the CPU use for each core drops, usually so that the total over all cores reaches 100%, i.e., as fast as if using just one single core fully. I tried my code directly from within emacs, and also using a

Hi, I'm trying to user lmer function from lmerTest package because, if I understood correectly, it allows to make better inference than lmer method from lme4 package. However, whatever I do I keep getting this error: Error in lme4::lFormula(formula = mark ~ ssCount + sTime+ : rank of X = 1660 < ncol(X) = 1895 any ideas what could be a problem? thanks, Srecko [[alternative HTML

Hello r-help, I've been banging my head against the computer in an attempt to learn how to divide my matrix into segments by rows. I want to be able to return each segment as a newly named object. I've tried looking at the apply functions and creating a for loop but brain no work. Here's the basic starting objects that I believe would be needed to separate the matrix. mat <-

Hi all: My OS is 32bit winxp,but I wanna install 64bit R2.14.1. >From the following website,it says "You can also go back and add 64-bit components to a 32-bit install, or vice versa" http://cran.r-project.org/bin/windows/rw-FAQ.html#Can-both-32_002d-and-64_002dbit-R-be-installed-on-the-same-machine_003f Does it mean that I can install and run 64bit R2.14.1 under 32bit