similar to: simulating the anova

I have been attempting some basic power calculations using R and I am not getting the results I expect. I had a homework assignment in SAS, but I want to learn R as well, so I was attempting to reproduce my result. (No one else in the class is doing R, so there's no need to obsfucate the answer, the SAS code is what I get my grade for.) The code I am using is: # You assume that the

Dear all, I have been trying to investigate the behaviour of different weights in weighted regression for a dataset with lots of missing data. As a start I simulated some data using the following: library(MASS) N <- 200 sigma <- matrix(c(1, .5, .5, 1), nrow = 2) sim.set <- as.data.frame(mvrnorm(N, c(0, 0), sigma)) colnames(sim.set) <- c('x1', 'x2') # x1 & x2 are

I sent a message a couple days ago about doing calculations for power of the ANOVA. Several people got back to me very quickly which I really appreciated. I'm working now on a similar problem, but instead of a balanced ANOVA, I have an unbalanced one. The first part of the question was: You assume that the within-population standard deviations all equal 9. You set the Type 1 error rate at á

Dear r-helpers, Spencer Graves and Manual Morales proposed the following methods to simulate p-values in lme4: ************preliminary************ require(lme4) require(MASS) summary(glm(y ~ lbase*trt + lage + V4, family = poisson, data = epil), cor = FALSE) epil2 <- epil[epil$period == 1, ] epil2["period"] <- rep(0, 59); epil2["y"] <- epil2["base"]

Dear list, This is more of a stats question than an R question per se. First, I realize there has been a lot of discussion about the problems with estimating P-values from F-ratios for mixed-effects models in lme4. Using mcmcsamp() seems like a great alternative for evaluating the significance of individual coefficients, but not for groups of coefficients as might occur in an experimental design

Hello all, I have the code below to simulate samples of certain size from a particular distribution (here,beta distribution) and compute some statistics for the samples. betasim2<-function(nsim,n,alpha,beta) { sim<-matrix(rbeta(nsim*n,alpha,beta),ncol=n) xmean<-apply(sim,1,mean) xvar<-apply(sim,1,var) xmedian<-apply(sim,1,median)

My grasp of R and statistics are both seriously lacking, so if this question is completely naive, I apologize in advance. I've hunted for a couple hours on the internet and none of the methods I've found have produced the result I'm looking for. I'm currently a student in a Statistics class and we are learning the ANOVA. We had to do one by hand and then reproduce our work in SAS.

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Hi, I'm trying to work through a Nested ANOVA for the following scenario: 20 males were used to fertilize eggs of 4 females per male, so that female is nested within male (80 females used total). Spine length was measured on 11 offspring per family, resulting in 880 measurements on 80 families. I used the following two commands: summary(aov(Spinelength ~ Male*Female)) and

I am trying to simulate a dataset using Parallel Latent CTT model and this is what i have done so far: (START) #Importing psych library for all the simulation related functions library(psych) # Settting the working directory path to C:/NCME path="C:/NCME" setwd(path) #Using the function to generate the data GenData <- congeneric.sim(N=500, loads =

Dear useRs, I have written an ecological model, based on the epidemiology SIR model. I've been trying to simulate it in R. However, I can't simulate it properly. Two guesses: my script isn't right; I'm not setting the parameters properly I have uploaded an image to the model here: http://img24.imageshack.us/img24/743/imagemutr.jpg The script I am using is as it follows:

Dear useRs, I want to simulate a time series (stationary; the distribution of values is skewed to the right; quite a few ARMA absolute standardized residuals above 2 - about 8% of them). Is this the right way to do it? #-------------------------------- load("rdtb") #the time series > summary(rdtb) Min. 1st Qu. Median Mean 3rd Qu. Max. -1.11800 -0.65010 -0.09091

I am having two problems creating data frames that I have solutions, but they really seem like kludges and I assume I just don't understand the proper R way of doing things. The first situation is I have an set of uneven data vectors. When I try to use them to create a data frame I would like the bottoms of them padded with NAs, without explicitly specifying that. When I do: anxiety.data =

Dear R-users, I would like to show you a simple example that gives an overview of one of my current issue. Although my working setting implies a different parametric model (which cannot be framed in the glm), I guess that what I'll get from the following example it would help for the next steps. Anyway here it is. Firstly I simulated from a series of exposures, a series of deaths (given a

Dear all, I have been looking for a while for ways to simulate from Langevin distributions and I thought I would ask here. I am ok with finding an algorithmic reference, though of course, a R package would be stupendous! Btw, just to clarify, the Langevin distribution with (mu, K), where mu is a vector and K>0 the concentration parameter is defined to be: f(x) = exp(K*mu'x) / const where

hello everybody I use the following code for my programming & it runs with the error as specified below.Any help that would disolve the error will be highly appreciated. Thanks in advance my code looks like this #### R programme for simulating the power of the two sample t test vs various #### non-parametric alternatives sim.size <- 200 sample.size <- 10 set.seed(231) mu1 <- 0 delta

Hi My simulation with the followin R code works perfectly: sim <- replicate(999, sum(exp(rgamma(rpois(1,2000), scale = 0.5, shape = 12)))) But now I do not want to have values in object "sim" exceeding 5'000'000, that means that I am just using the beginning of densitiy function gamma x < 15.4. Is there a possibility to modify my code in an easy way? Thanks for any help!

When I try to create a Rasch simulation of data using the sim.rasch function, I get more items than I intend #My code library(eRm) #Number of items k <- 20 #Number of participants n <- 100 #Create Rasch Data #sim.rasch(persons, items, seed = NULL, cutpoint = "randomized") r.simulation <- sim.rasch(n,k) I end up with 20 participants and 100 items, but the instructions say

On 1/29/2018 9:03 PM, smart hendsome via R-help wrote: > Hello everyone, > I have a question regarding simulating based on runif.? Let say I have generated matrix A and B based on runif. Then I find mean for each matrix A and matrix B.? I want this process to be done let say 10 times. Anyone can help me.? Actually I want make the function that I can play around with the number of simulation

Hi, This report deals with p-values coming from chisq.test using the simulate.p=TRUE option. The issue is numerical accuracy and was brought up in previous bug reports 3486 and 3896. The bug was considered fixed but apparently was only mostly fixed. Just the typical problem of two values that are mathematically equal not ending up numerically equivalent. Consider this series of three 2x2