similar to: mixed normal distriburtion

I would be very grateful for any help from members of this list for what might be a simple problem... We are trying to simulate the behaviour of a clinical measurement in a series of computer experiments. This is simple enough to do in R if we assume the measurements to be Gaussian, but their empirical distribution has a much higher peak at the mean and the distribution has much longer tails.

During norml operation this morning we're seeing a significantly increased load on the IMAP server (factor 5, from 5 to 25). Does dovecot need to rebuild index files or perform other IO intensive operations after the upgrade? We're using Maildir Our monitoring shows increased "system" time. We're runnign fewer processes, the IO rate ist identical to yesterday, memory usage

Dear R-helpers, I am developing a Mixed-Effects model for a study of immunoassays using 'lme4' library. The study design is as follows: 10 samples were run using 7 different immunoassays, 3 times each, in duplicates. Data attached. I have developed the following model: c.lme <- lmer(Result~SPL + (SPL|Assay/Run) -1, data=data) This model has excellent predictions - the Rsquared of

Dear list, I have a dataset containing values obtained from two different instruments (x and y). I want to generate 5 samples from normal distribution for each instrument based on their means and standard deviations. The problem is values from both instruments are non-negative, so if using rnorm I would get some negative values. Is there any options to determine the lower bound of normal

Hello, JIT compiler interferes with internal R completions: compiler::enableJIT(2) utils:::functionArgs("density", '') gives: utils:::functionArgs("density", '') Note: no visible global function definition for 'bw.nrd0' Note: no visible global function definition for 'bw.nrd' Note: no visible global function definition for 'bw.ucv'

Is it in bad form to double post to StackOverflow and R-help? Apologies if so. Here's my task: I've got a matrix of means like so means<-matrix(1:10,nrow=2) colnames(means)<-c("a","b","c","d","e") and a matrix of standard deviations like so sds<-matrix(seq(0.1,1,by=0.1),nrow=2)

Hi, I'm have a list of integer vectors and I want to perform an outer() like operation on the list. As an example, take the following list: mylist <- list(1:5,3:9,8:12) A simple example of the kind of thing I want to do is to find the sum of the shared numbers between each vector to give a result like: result <- array(c(15,12,0,12,42,17,0,17,50), dim=c(3,3)) Two for() loops is the

Dear all R users, Please forgive me if my question is too trivial. Suppose I have two variables, (x,y) which is log-normally distributed with expected value (mu1, mu2) and some variance-covariance matrix. Now I want to draw a random sample of size 1000 from this distribution. Is there any function available to do this? Thanks and regards, Megh

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

Hi, Can anyone help me with the following. I have been using R for Monte Carlo simulations and got some results I couldn't explain. Therefor I performed following short test: -------------- mean.sds <- NULL sample.sizes <- 3:30 for(N in sample.sizes){ dum <- NULL for(I in 1:5000){ x <- rnorm(N,0,1) dum <- c(dum,sd(x)) } mean.sds<- c(mean.sds,mean(dum)) }

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)

Hi all, I'm using the function "fitdistr" (library MASS) to fit a distribution to given data. What I have to do further, is getting the log-Likelihood-Value from this estimation. Is there any simple possibility to realize it? Regards, Carsten

Hi all, I am trying to find sd of my rows in a matrix and i get column sd inspite of extracting rows. I tried to do the sqrt(var(x)) but that did'nt work as well, Here is my data genes 15 24 63 40 25 42 46 35 23 53 37 45 30 37 50 55 40 51 30 48 x<-sd(genes[1:5,]) y<-sqrt(var(genes[1:5,])) I get 4 sds for the 4 columns instead of 5 sds for my 5 rows. Thanks you in advance.

Dear all, I am trying to use R to fit mixed models. Take the following example, where ind is a random effect and sample is fixed. I wanted to fit Model 1: values = ind + sample Model 2: values =ind * sample Model 3: values=ind(sample) + sample Tried to use the below for mod1, but it did not work. Can anyone give some help on this. Thanks so much. mod1 <- lme(values ~ sample +

Hello, I am trying to plot a curve over points plotted with se's in xYplot (see example below). I can get Figure 1 below to plot the data with error. However, I keep getting a the error message "Error using packet 1 object "y" not found" Can anyone see what I am doing wrong? Thanks! John ------------------------------- a=0.002; b=31.7; c=0.51

(summary.default): show the vector length in addition to quantiles diff -u -i -p -F '^(def' -b -w -B /home/sds/src/R-3.0.1/src/library/base/R/summary.R.old /home/sds/src/R-3.0.1/src/library/base/R/summary.R --- /home/sds/src/R-3.0.1/src/library/base/R/summary.R.old 2013-03-05 18:02:33.000000000 -0500 +++ /home/sds/src/R-3.0.1/src/library/base/R/summary.R 2013-09-10 10:19:02.682946339

Hi, I just came across the following issue regarding mixed effects models: In a longitudinal study individuals (variable ind) are observed for some response variable. One explanatory variable, f, entering the model as fixed effect, is a (2-level) factor. The expression of that factor is constant for each individual across time (say, the sex of the individual). ind enters the model as grouping

Hi all, Very surprising (to me!) and mystifying result from predict.glm(): the predictions vary depending on whether or not I use ns() or splines::ns(). Reprex follows: library(splines) set.seed(12345) dat <- data.frame(claim = rbinom(1000, 1, 0.5)) mns <- c(3.4, 3.6) sds <- c(0.24, 0.35) dat$wind <- exp(rnorm(nrow(dat), mean = mns[dat$claim + 1], sd = sds[dat$claim + 1])) dat <-

Hi, to count vector elements with some property, the standard idiom seems to be length(which): --8<---------------cut here---------------start------------->8--- x <- c(1,1,0,0,0) count.0 <- length(which(x == 0)) --8<---------------cut here---------------end--------------->8--- however, this approach allocates and discards 2 vectors: a logical vector of length=length(x) and an

How do I convert a list to a matrix? --8<---------------cut here---------------start------------->8--- list(c(50000, 101), c(1e+05, 46), c(150000, 31), c(2e+05, 17), c(250000, 19), c(3e+05, 11), c(350000, 12), c(4e+05, 25), c(450000, 19), c(5e+05, 16)) as.matrix(a) [,1] [1,] Numeric,2 [2,] Numeric,2 [3,] Numeric,2 [4,] Numeric,2 [5,] Numeric,2 [6,] Numeric,2 [7,]