similar to: need some help finding power in test about variances

Hello All I am new to this list. I have a problem where for a single sample drawn from normal population, null hypothesis is that variance = k (say). Alternative hypothesis is variance > k. Now if we know the true variance, then I would like to calculate the sample size required to produce certain power (for some significance). How do I do this ? I thought of using pwr.chisq.test, and I

Dear all, I've been delighted to just notice that Cohen's formulas for Effect Size 'w' and the associated power have been implemented in the 'pwr' package (thanks to St?phane Champely and others).. There is one aspect, though, that perplexes me. I'm doing some last minute post hoc analyses, meaning that my sample size (N=3404) has been long fixed, and I'm

Craig, Thanks for your follow-up note on using the asypow package. My problem was not only constructing the "constraints" vector but, for my particular situation (Poisson regression, two groups, sample sizes of (1081,3180), I get very different results using asypow package compared to my other (home grown) approaches. library(asypow) pois.mean<-c(0.0065,0.0003) info.pois <-

Hi There, There are 300 boy students and 100 girl students in a class. One interesting question is whether boy is smarter than girl or not. first given the exam with a difficulty level 1, the number of the student who got A is below 31 for boy, 10 for girl. Then we increase the difficulty level of the exam to level 2, the number of the student who got A is below 32 for boy, 10 for girl. We

Hi everyone, Suppose I have a count the occurrences of positive results, and the total number of occurrences: pos <- 14 total <- 15 testing that the proportion of positive occurrences is greater than 0.5 gives a p-value and confidence interval: prop.test( pos, total, p=0.5, alternative='greater') 1-sample proportions test with continuity correction data: 14 out of

Hi to everybody. I use the following routine (i found it in the internet) to compute the Hosmer-Lemeshow test in the framework of logistic regression. hosmerlemeshow = function(obj, g=10) { # first, check to see if we fed in the right kind of object stopifnot(family(obj)$family=="binomial" && family(obj)$link=="logit") y = obj$model[[1]] # the double bracket

Hi, I have a text mining project and currently I am working on feature generation/selection part. My plan is selecting a set of words or word combinations which have better discriminant capability than other words in telling the group id's (2 classes in this case) for a dataset which has 2,000,000 documents. One approach is using "contrast-set association rule mining" while the

dear all, in the package pwr , there is the fonction power.anova.test which permit to obtain the power for a one-way ANOVA...but I'm looking for a way to compute the power of a multiway ANOVA.( find the 1-beta). Is it possible? do you have some ideas ? regards [[alternative HTML version deleted]]

On Wed, 4 Jan 2006, Peter Muhlberger wrote: One comment in advance: please use a more meaningful subject. I would have missed this mail if a colleague hadn't pointed me to it. > I'm someone who from time to time comes to R to do applied stats for social > science research. [snip] > I would also prefer not to have to work through a > couple books on R or S+ to learn how to

Full_Name: Michael J. Lew Version: R version 2.9.1 (2009-06-26) OS: OS X Submission from: (NULL) (210.49.195.149) The default function power.t.test gives inaccurate values, particularly with large sig.level. There are two reasons for me to propose that power.t.test is inaccurate: 1. When sig.level approaches 1, so should the power. However,

Hi All, I woul like to ask you a couple of questions on chisq.test. First, I have 40 flies, 14 males and 26 females and I want to test for an a priori hypothesis that the sex ratio is 1:1 sex<-c(14,26) pr<-c(1,1)/2 chisq.test(se, p=pr, correct=TRUE) Chi-squared test for given probabilities data: sex X-squared = 3.6, df = 1, p-value = 0.05778 If my calculations are correct, this is

Dear all, I have a dataset with qualitative variables (factors) and I want to test the null hypothesis of independance between two variables for each pair by using appropriate tests on contingency tables. I first applied chisq.test and obtained dependance in almost all cases with extremely small p-values and warning messages. > chisq.test(table(data$ins.f, data$ins.st))$p.val [1]

Dear all If I run the model and get the estimated parameter a11. Then I want to use the estimated parameter to calculate the elasticities by using the formula e11=a11/mw1-1. What I have done is using the command of linear. Hypothesis. > formulas1=dWfresh~dlnPfresh+dlnPfrozen+dlnPsmoke+dlnQP+cosL1+sinL1+cosL2 >

I am interested in knowing whether and how I can test the significance of the relationship between my continuous predictor variable (a covariate) and my binary response variable according to two different groups, my categorical predictor variable, in a logistic regression model (glm). Specifically, can I determine whether the relationships are identical (the hypothesis of coincidence), or whether

Dear R experts, I have conducted a power calculation in order to estimate the number of subjects needed to detect an effect size of d=0.28 (cohen's d) for a difference between two independent groups (alpha level should be 0.05 and the effect should be detected with 80% probability). The results from the code below indicates that I would need n=400 subjects (200 in each group). This is seems

dear helplist, my student has fifty trees, numbered one to fifty, and a vector recording which tree a certain possum slept in on 12 nights. R> c [1] 3 14 17 22 26 26 17 40 43 25 46 46 R> Thus it slept in tree #3 on Monday, then tree #14 on Tues, and so on. I wish to test the null hypothesis that the animal chooses trees randomly; try R> table(c) c 3 14 17 22 25 26 40 43 46 1 1

I am testing the homogeneity of variances via bartlett.test and fligner.test. Using the following example, how should I interpret the p-value in order to accept or reject the null hypothesis ? set.seed(5) x <- rnorm(20) bartlett.test(x, rep(1:5, each=4)) Bartlett test of homogeneity of variances data: x and rep(1:5, each = 4) Bartlett's K-squared = 1.7709, df = 4, p-value =

Dear R-User, I met the problem to test equality of variance. Two sample units: conjps<-c(9.41,10.45,10.78,10.73,11.11,11.12,11.59,11.04,11.63) ms<-c(4.11,5.10,5.70,6.46,6.04,6.16, 6.24,6.32,7.33) Then I use the F test to test: •Test Equality of Two Variances F test to compare two variances data: conjps and ms F = 0.5419, num df = 8, denom df = 8, p-value = 0.4045 alternative

Hi all, I'm having trouble getting access to the pwr. This is on Ubuntu Lucid Lynx, 64 bit. I'm installing pwr via packages.install('pwr'), and loading it via library(pwr), both of which appear successful. Strangely, I never get access to the pwr object in R. I tried installing it to /usr/local/lib/R/site-library and ~/R/x86_64-pc-linux-gnu-library. I also tried installing it

Hello, I have a question concerning the R-function chisq.test. For example, I have some count data which can be categorized as follows class1: 15 observations class2: 0 observations class3: 3 observations class4: 4 observations I would like to test the hypothesis whether the population probabilities are all equal (=> Test for discrete uniform distribution) If you have a small sample size