similar to: Examples of hypothesis testing Bryan Moss

Hi, This is an announcement for a package that has been up on CRAN since March 2006 but was never announced. The package is Mchtest - for Monte Carlo hypothesis tests allowing sequential stopping. The idea is to use the sequential probability ratio test boundaries to stop resampling for a Monte Carlo hypothesis test such as a bootstrap or permutation test. This means that you will take many

Hi, This is an announcement for a package that has been up on CRAN since March 2006 but was never announced. The package is Mchtest - for Monte Carlo hypothesis tests allowing sequential stopping. The idea is to use the sequential probability ratio test boundaries to stop resampling for a Monte Carlo hypothesis test such as a bootstrap or permutation test. This means that you will take many

Hi R-users! I have the following example: a<-data.frame(cat=c(5,10,15), dog=c(5,10, 15), mouse=c(10,10,20)) b<-data.frame(cat=c(15,10,5), dog=c(15, 10, 5), mouse=c(20,10,10)) rownames(b)<-c("scared", "happy", "sad") rownames(a)<-c("scared", "happy", "sad") Let's say that a and b are 2 contingency tables and 5,10, 15, 20

Bryan Green has subscribed to the ML, but is getting his postings rejected. I''m forward his post to the list for you to read, and as a test to see if I also get an error. Bryan: Can you send me the actual error/rejection-notice you are getting? Anyway, here''s Bryan''s message: Greetings to everyone! I have been pointed to this project from the Freeride project. I

Hello, I have a MANOVA model and I want to test the following hypothesis: LBM = 0 where B is the parameter estimates. Is there any function to do this in R? Cheers, Philippe -- Philippe Hup? Institut Curie, CNRS UMR 144, INSERM U900 26 rue d'Ulm 75005 Paris - France Email : Philippe.Hupe at curie.fr T?l : +33 (0)1 56 24 69 91 Fax: +33 (0)1 56 24 69 11 website :

Hi, I hope to use R to perform some hypothesis testing, such as t.test() and var.test(). However, I don't find a function that could do test on means with variance known (i.e., u test or z test in some textbook), and a function that could do test on variances of normal distribution (i.e. chi-squared test). Thanks in advance for any hints. Best wishes, Jinsong

I have two models, controlled by dummy variables to see if the models can be combined into one model with similar intercepts and slopes. Has anyone tried to conduct this type of test in R. I am utilizing the econometric idea of hypothesis testing through the hypothesis of coincidence. I have tried to run an anova with test of Chisq, but I am not sure what the results are telling. In addition, I

I wish to test the hypothesis of mu equal to or less than 5 against the specific alternative mu equal to or greater than 7. I am unable to find how to persuade R to do this with any function (e.g. t.test). Suggestions? Michael Grant [[alternative HTML version deleted]]

Hi folks: Does anyone know of a way to do (linear) hypothesis tests of parameters after fitting a maximum-likelihood model w/ optim? I can't seem to find anything like a Wald test whose documentation says it applies to optim output. Also, thanks again to everyone who gave me feedback on the robustness of ML estimation in R! Peter ********************************

Is this how a t hypothesis test is done when I don't have the actual data, but just the summarized statistics: > #Homework 9.2.6 [1] > n<-31 > xbar<-3.10 > s_x<-1.469 > m<-57 > ybar<-2.43 > s_y<-1.35 > s_pooled<- (((n-1)*s_x^2) + ((m-1)*s_y^2)) / (n + m - 2) > s_pooled [1] 1.939521 > t_obs <- (xbar - ybar) / (s_pooled * (sqrt(1/n + 1/m)))

Dear all, Being an economics student, I am trying to put together a little tutorial/FAQ/... for those who have (more) background in econometrics rather than in statistics -- just as I have. I'v been looking for a hypothesis testing tool in R (just linear or also nonlinear) for model parameters, but could not find anything so far. I did find a similar but unanswered question in the archives,

In summary.glm I'm trying to get a better feel for the z output. The following lines can be found in the function 1 if (p > 0) { 2 p1 <- 1:p 3 Qr <- object$qr 4 coef.p <- object$coefficients[Qr$pivot[p1]] 5 covmat.unscaled <- chol2inv(Qr$qr[p1, p1, drop = FALSE]) 6 dimnames(covmat.unscaled) <- list(names(coef.p), names(coef.p))

R Users- I have been trying to automate a manual code that I have developed for calling in a .csv file, isolating certain rows and columns that correspond to specified months: something to the effect i=name.csv N=length(i$month) iphos1=0 iphos2=0 isphos3=0 for i=1,N if month=1 iphos1=iphos+1 iphos1(iphos1)=i an so on to call out the months into there own arrays (unless there is a way I

From: R P Herrold <herrold at owlriver.com> > Bryan - Please don't overstate this and make a problem where > none exists. There is an infinity of choices of which you > list three ... > Nor does one _have_ to 'get involved' with either the > RH-Fedora or Centos project to do effective distribution > building, or add-on packaging. There are independent

From: Collins Richey <crichey at gmail.com> > It's a little more than that. I find few people who consider RedHat to > be a Demon Evil or that they can't do anything right, but I can > understand the concern about some of their decisions which have made > life difficult for (granted) a few, and it's not really helpful to > demonize those who complain, as Bryan has

Dear R developers, Currently many (all?) test functions in R describe the alternative hypothesis, but not the the null hypothesis being tested. For example, cor.test: > require(boot) > data(mtcars) > with(mtcars, cor.test(mpg, wt, met="kendall")) Kendall's rank correlation tau data: mpg and wt z = -5.7981, p-value = 0.000000006706 alternative hypothesis: true tau is not

Good morning! I have 4 questions which trouble me: 1. I want to test the hypothesis that the 2 proportions (the mean of a binomial) which come from 2 different samples are equal. I want to use the following function z= (p1-p2)/ sqrt((p1(1-p1)/n1)+(p2(1-p2)/n2)) which is one of the standard formulas for this case. Is there such a function in R? p1=the proportion from the first sample n1=the

Dear R community, Confused by some of my lab results I ask for the definition of the null hypothesis of a two-way analysis of variance in R (anova() and aov()). Starting with the following model y = a_i + b_j , i in A and j in B is the tested null hypothesis H_0: a_i = 0 for all i in A or H_0: a_m = a_n for any m and n in A? Consequently the same questions for interaction effects.

I want to specify a 2-level mixed model using the lme function in order to test an a priori hypothesis about the between-group values of the slopes but don't know how to do this . Here is the problem. Consider first the case of a single group. The model is: Y_i= a +bX_i + error where I indexes the different values of X and Y in this group . The a priori hypothesis of the slope is: b=K.

Dear R users, I am analyzing several response variables (all scaled to [0;1]) using a multivariate linear model. After fitting the model, I set up a hypothesis matrix to test specific contrasts for these response variables; for example: "a always increases significantly more than b when regressed against x". What I am stuck with now is how to calculate the correct F-values (and