similar to: Confidence Limits for a Cross-Product Ratio

Hi I am replacing my current mail system, using Exim/Postgres/WU-IMAP to another host using Dovecot. Everything has gone well, until I have tried to access some test mails via POP. The error message I am getting is Feb 14 03:03:57 wpc1273 pop3(simonw at orion-w-t.co.uk): lstat (/home/dovecot/users/orion-w-t.co.uk/simonw/cur) failed: Permission denied Feb 14 03:03:57 wpc1273 pop3(simonw at

Hi I have just upgraded a mail server from dovecot-0.99 to stable (dovecot-1.0.59). My normal desktop email client, kmail is working fine, but outlook keeps producing PLAIN authentication errors, and refusing to authenticate. From dovecot.log, I can see dovecot: Mar 13 10:57:30 Error: auth(default): sql(<xxxxxxxMy Detailsxxxxxxxxxxx>): Password query must return a field named

The concrete problem is that I am refereeing a paper where a confidence interval is presented for the risk ratio and I do not find it credible. I show below my attempts to do this in R. The example is slightly changed from the authors'. I can obtain a confidence interval for the odds ratio from fisher.test of course === fisher.test example === > outcome <- matrix(c(500, 0, 500, 8),

I'm wondering how odds ratio is computed. I thought that it is (n11/n12)/(n21/n22), but it is not what fisher.test() computes. Could somebody let me know? > n11=3 > n12=1 > n21=1 > n22=3 > > n1_=n11+n12 > n2_=n21+n22 > > n_1=n11+n21 > n_2=n12+n22 > > x=rbind(c(n11,n12),c(n21,n22)) > > threshold=dhyper(n11,n1_,n2_,n_1) >

Hello: Please help me through my confusion. I am having trouble reconciling the difference between what I believe is the conventional definition of an odds ratio for a 2-by-2 table and the output produced by fisher.test() in R. Consider the following example: > Discrim <- matrix(c(1,10,24,17), + nr = 2, + dimnames = list(AGE = c('young', 'old'), +

Peter & Michael, I just came across the following on another mailing list and realized that my use (and the authors of the article use of the term 'odds ratio' ) is probably incorrect. I believe my interest is in the 'odds' of schizophrenia among the population of homicide, rather than a comparison of odds . "Yours seems a good idea, Kevin, if you are only interested

(1) So finally, thank to your help I have this: summary(lm(x ~ 0+I(t^2))) And then I get this result: ================================================= Call: lm(formula = x ~ 0 + I(t^2)) Residuals: Min 1Q Median 3Q Max -3.332e-02 -9.362e-03 1.169e-05 1.411e-02 3.459e-02 Coefficients: Estimate Std. Error t value Pr(>|t|) I(t^2) 0.0393821

Timothy, I believe you are mistaken. Fisher's exact test give the correct answer even in the face of small expected values for the cell counts. Pearson's Chi-square approximates Fisher's exact test and can give the wrong answer when expected cell counts are low. Chi-square was developed because it is computationally "simple". Fisher's exact test, particularly with tables

Hi everyone, I'm trying to solve a problem about how to get the Fisher's discriminant functions of a "lda" (linear discriminant analysis) object, I mean, the object obtained from doing "lda(formula, data)" function of the package MASS in R-project. This object gives me the canonical linear functions (n-1 coefficients matrix of n groups at least), and only with this

Hi all, I have just uploaded a new package to the ftp site called exact2x2. The package does exact conditional tests for 2x2 tables. Specifically, it does Fisher's exact test and Blaker's exact test (see Blaker, 2000, Canadian Journal of Statistics, 783-798). The motivation for the package is to have inferences from p-values and confidence intervals match as much as is possible for the

Hi all, I have just uploaded a new package to the ftp site called exact2x2. The package does exact conditional tests for 2x2 tables. Specifically, it does Fisher's exact test and Blaker's exact test (see Blaker, 2000, Canadian Journal of Statistics, 783-798). The motivation for the package is to have inferences from p-values and confidence intervals match as much as is possible for the

Full_Name: Martha Nason Version: 2.0.1 OS: Windows XP Submission from: (NULL) (137.187.154.154) I am running simulations using fisher's test on 2 x c tables and a very small p.value from fisher's test (<2.2e-16) is returned as a negative number. Code follows. > set.seed(0) > nreps.outer <-7 > pvalue.fisher <- rep(NA,nreps.outer) > > population1 <- c(

Hi, I am trying to solve a discriminant analysis in the same way as SPSS does it. I mean, given an amount of data, to train the discriminant analysis I obtain the Fisher's discriminant functions, an array of coefficients per group, so if I have 8 groups I get 8 linear functions, that allow me to operate with them easily and without a great cost of time. My main problem is that I need to

Hi all i have two data sets, spot and futures cash market prices. to estimate the minimum variance hedge ratio, i first had a glance on the correlation coefficient of relative price change (ln(St / St-1). surprizingly the value is just 0.2 compared to actual price correlation of 0.9. (i did regress the spot change on future change, co-effi is 0.3, and R2 is only 0.025 a) in such scenario can

(Sorry if this is a repost, I got a bounce reply from the r-help server) Hi, I’m using the biglm() function to create some linear models for a very large data set than lm() can’t fit due to memory issues (the problem is with the number of interactions, I can fit the main effects model) I need to determine if the 2-way interactions are necessary or not. Ideally I’d like to use anova() to

Hi all, I am trying to calculate the variance ratio of a time series under heteroskedasticity. So I know that the variance ratio should be calculated as a weighted average of autocorrelations. But I don't find the same results when I calculate the variance ratio manually and when I compute the M2 (M2 for heteroskedasticity) variance ratio using Lo.Mac function in R. Anybody knows what

Hello, I am looking for a code in R for the variance ratio test statistic (the Lo and Mackinlay version or any other versions). Does anybody have such a code they can share or know a library in which I can find this function? Basically I have a number of time series which I need to check for persistence. One other test I can use is the runs test in the tseries package. Any help will be greatly

Ms.Karunambigai M PhD Scholar Dept. of Biostatistics NIMHANS Bangalore India ----- Forwarded Message ---- From: karuna m <m_karuna2002@yahoo.com> To: R-help@r-project.org Sent: Thu, 26 May, 2011 10:55:02 AM Subject: likelihood ratio test Dear R-help, Can anybody tell me which R package has Lo-Mendell Rubin LR test and Bootstrap LR test to compare the model fit between k class and k+1

It seems to me that R returns the unpenalized log-likelihood for the ratio likelihood test when ridge regression Cox proportional model is implemented. Is this as expected? In the example below, if I am not mistaken, fit$loglik[2] is unpenalized log-likelihood for the final estimates of coefficients. I would expect to get the penalized log-likelihood. I would like to check if this is as expected.

I'm applying logistic regression to a moderate sized data set for which I believe Wald based confidence intervals on B coefficients are too conservative. Some of the literature recommends using confidence intervals based on the likelihood ratio in such cases, but I'm having difficulty locating a package that can do these. Any help would be immensely appreciated. Best, Jeff Hanna --