similar to: Help on calculating a variable using random numbers

I have the following script for generating a dataset. It works like a champ except for a couple of things. 1. I need the variables "itbs" and "map" to be negatively correlated with the binomial variable "lunch" (around -0.21 and -0.24, respectively). The binomial variable "lunch" needs to remain unchanged. 2. While my generated variables do come out

Hello, R friends... I am very new to R, and I need some help. I am trying to construct a simulation for my dissertation. I need to create 1000 datasets of 1000 subjects with the following variables... Treatment variable - Drawn from a binomial distribution (1 run, prob=.13) Covariate 1 - Drawn from a normal distribution (mean=100, sd=16) Covariate 2 - Drawn from a normal distribution

I have a categorical variable in a dataframe similar to the following... cat 1 1 3 2 4 I need to convert it to 4 dichotemous variables for each observations like... cat1 cat2 cat3 cat4 1 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 Thanks in advance! Shane

# R for Windows will not send your bug report automatically. # Please copy the bug report (after finishing it) to # your favorite email program and send it to # # r-bugs at r-project.org # ###################################################### This report is joint from Richard Heiberger <rmh at temple.edu> and Burt Holland <bholland at temple.edu>. Burt Holland is the coauthor

Dear R-users I analysed the same data set by two different ways; analysis of covariance by using lm and anova functions and multiple comparison by using simtest function in the multcomp library. The output from the analysis of covariance is; > y<-lm(D~Cond+Q1,data=x) > anova(y) Analysis of Variance Table Response: D Df Sum Sq Mean Sq F value Pr(>F) Cond 2

Hi, I've got a problem building a contrast matrix for the Dunnet contrast in package multcopm. The following works fine: > summary(simtest(adiff ~ trial)) Simultaneous tests: Dunnett contrasts Data: adiff by trial Contrast matrix: trial1 trial2 trial3 trial4 trial5 trial2-trial1 -1 1 0 0 0 trial3-trial1 -1 0 1 0 0

Hi, all: Two examples are shown below. I want to use the multiple comparison of Dunnett. It succeeded in upper case "example 1". However, the lower case "example 2" went wrong. In "example 2", the function pmvt return NaN, so I cannot show this simtest result. Is there any solution? (I changed the variable "maxpts" to a large number in front of the

Hi! I use simtest fonction of multcomp package to compile a Dunnett's test. I have 10 treatments and one control group, so i create a matrix with: m<-matrix(0,10,11) m[1,1]<--1 m[1,2]<-1 m[2,1]<--1 m[2,3]<-1 m[3,1]<--1 m[3,4]<-1 m[4,1]<--1 m[4,5]<-1 m[5,1]<--1 m[5,6]<-1 m[6,1]<--1 m[6,7]<-1 m[7,1]<--1 m[7,8]<-1 m[8,1]<--1 m[8,9]<-1

I've been playing around with various table tools, trying to construct a fairly simple cross-tab. It shouldn't be hard, but for some reason it turning out to be (for me). If I want to see how many men and how many women agree with a agree/disagree question (coded 1,0), I can do this: >attach(mydata) >mytable <- table(male, q1.bin) # gender and a binary response variable

Dear WizaRds, I am sorry to bother you with a newbie question, but although I tried to solve my problem using the various .pdf files (Introduction, help pages etc.), I have come to a complete stop. Please be so kind as to guide me a little bit along my way of exploring multivariate analysis in R. I want to test wether the means-vector mu1 of X, consisting of the means per column of that matrix

Hello all I am working with the package multcomp but I have problems with the function simtest; the program say that can not find this function, nevertheless I doesn't have any problem with the function glht that it is in the same package. Someone knows what could be the problem? Thank you [[alternative HTML version deleted]]

Dear R-listers, I am trying to compute simultaneous confidence intervals with simint from the package multcomp. 230 measures (abundance) have been taken in 23 sites (factor) of a data.frame (donnees: a file can be sent on request, saved with save(donnees,file="donnees")). I would like to get all pairwise comparisons with : mc<- simint(ren~ID,type="Tukey",data=donnees) I

hello, after an anova I use pairwise.t.test(), it gives only p.value and I want the t.stat. I try to get these by computing the Welch approximation of the degree of freedom and using the qt(p.value,df) function but when I test this method with t.test results (the function gives p.value and t.test), I doesn't find the same t.stat. I also use the simtest(x~y,type="Tukey") function

Hi, All, This is my program ts1.sim <- arima.sim(list(order = c(1,1,0), ar = c(0.7)), n = 200) ts2.sim <- arima.sim(list(order = c(1,1,0), ar = c(0.5)), n = 200) tdata<-ts(c(ts1.sim[-1],ts2.sim[-1])) tre<-c(rep(0,200),rep(1,200)) gender<-rbinom(400,1,.5) x<-matrix(0,2,400) x[1,]<-tre x[2,]<-gender fit <- arima(tdata, c(1, 1, 0), method = "CSS",xreg=t(x))

Hi,. We have 2 values (first formant F1, second formant F2) for a given phoneme for six languages. We want to see whether the languages are significantly different one from another for this given phoneme. We have done a manova on our data and it works well, but we doesn't allow us to see which pair of languages are different. If we have only one formant for the phoneme, we would use

hello, I have a problem in how to generate data in a simulation study. I have a logistic model to evaluate p by 3 covariates. I need to generate 4 variables: the binary outcome Y and 3 covariates: gender (binary) and aps and tiss (continuous variables). I have the logistic model which is the expected model: log(p(y=1)/(1-p(y=1))=-1.659-0.05*sex+0.063*aps+0.04*tiss0) I generate the outcome y like

hi list, i have to ask you again, having tried and searched for several days... i want to do a TukeyHSD after an Anova, and want to get the adjusted p-values after the Tukey Correction. i found the p.adjust function, but it can only correct for "holm", "hochberg", bonferroni", but not "Tukey". Is it not possbile to get adjusted p-values after

Dear R-users, We expect to develop statistic procedures and environnement for the computational analysis of our experimental datas. To provide a proof of concept, we plan to implement a test for a given experiment. Its design split data into 10 groups (including a control one) with 2 mesures for each (ref at t0 and response at t1). We aim to compare each group response with control response

Hi. I was wondering if anyone might have some suggestions about how I can overcome a problem of "iteration limit reached without convergence" when fitting a mixed effects model. In this study: Outcome is a measure of heart action Age is continuous (in weeks) Gender is Male or Female (0 or 1) Genotype is Wild type or knockout (0 or 1) Animal is the Animal ID as a factor

dear all, I'm building new dataframes from bigger one's using e.g. columns F76, F83, F90: JJ<-data.frame( c( as.character(rep( gender,3))) , c( F76,6- F83, F90) ) Looking into JJ one has: c.as.character.rep.gender..8... c.6...F73..F78..F79..F82..6...F84..F94..F106..F109 1 w 2 2 w