similar to: Update to exact2x2 package: Exact McNemar's test

Hi all, I have updated the exact2x2 package. The update corrects an error that can sometimes occur in the confidence intervals when the number of counts are large and tsmethod="minlike". In addition to fixing the bug, the correction gives a much faster method for calculating the confidence interval. The package gives exact confidence intervals that match the p-values for Fisher's

Hi all, I have updated the exact2x2 package. The update corrects an error that can sometimes occur in the confidence intervals when the number of counts are large and tsmethod="minlike". In addition to fixing the bug, the correction gives a much faster method for calculating the confidence interval. The package gives exact confidence intervals that match the p-values for Fisher's

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

Hi, I received a bug report for my exact2x2 package that seems to apply to fisher.test in the stats package also. Here is some code which creates the error: >x<-factor(c(0,1,1),levels=c(0,1)) > y<-factor(c(1,1,1),levels=c(0,1)) > fisher.test(x,y) Error in fisher.test(x, y) : 'x' and 'y' must have at least 2 levels > The help says that x and y could be

Hi all, I just uploaded a new version of the bpcp package. It calculates confidence intervals for a survival distribution for right-censored data using the newly developed beta product confidence procedure. Previously developed methods can have substantial error rate inflation for the lower limit, especially at the right end of the curves when there are small numbers of events. The bpcp method

Hi, The poisson.test function from stats package does not pass the conf.level p= arameter for the two-sample test. Here is an example: poisson.test(c(2,4),c(20,14),conf.level=3D.95)$conf.int poisson.test(c(2,4),c(20,14),conf.level=3D.9)$conf.int Here is the solution, change: RVAL <- binom.test(x, sum(x), r * T[1]/(r * T[1] + T[2]), alternative =3D alternative) to:

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, This is a late announcement for a package that has been up on CRAN since Feb 2006. The package is called ssanv and it calculates sample sizes for two group tests with adjustments for nonadherence and variability of the input parameters. As an example, suppose you want a sample size for a two group difference in normal means but you get your estimate of your variance from a previous study.

Hi, This is a late announcement for a package that has been up on CRAN since Feb 2006. The package is called ssanv and it calculates sample sizes for two group tests with adjustments for nonadherence and variability of the input parameters. As an example, suppose you want a sample size for a two group difference in normal means but you get your estimate of your variance from a previous study.

Hi, I just uploaded a new package rateratio.test. The package contains one function of the same name and it performs an exact rate ratio test for Poisson counts. Unlike binom.test and fisher.test the p-values and confidence intervals are internally consistent. In other words, if the p-value implies that the null hypothesis should be rejected, then the confidence interval also implies that the

Hi, I just uploaded a new package rateratio.test. The package contains one function of the same name and it performs an exact rate ratio test for Poisson counts. Unlike binom.test and fisher.test the p-values and confidence intervals are internally consistent. In other words, if the p-value implies that the null hypothesis should be rejected, then the confidence interval also implies that the

Hi all, I have uploaded an update of the interval package, which calculates nonparametric maximum likelihood estimates (NPMLE) of distributions and exact or asymptotic weighted logrank-type tests for interval censored data. An important part of the update is that now the default initial estimate of the NPMLE uses the MLEcens package and is much faster. This increases the speed of the logrank-type

Hi all, I have uploaded an update of the interval package, which calculates nonparametric maximum likelihood estimates (NPMLE) of distributions and exact or asymptotic weighted logrank-type tests for interval censored data. An important part of the update is that now the default initial estimate of the NPMLE uses the MLEcens package and is much faster. This increases the speed of the logrank-type

I am announcing the release of the exactci package. It calculates exact tests and confidence intervals for binomial and Poisson tests. Here is an example to motivate the package: Suppose you want to see if the observed rates of 2/17877 for group A are significantly different from the observed rates of 10/20000 for group B assuming Poisson counts. The poisson.test function in the stats package

I am announcing the release of the exactci package. It calculates exact tests and confidence intervals for binomial and Poisson tests. Here is an example to motivate the package: Suppose you want to see if the observed rates of 2/17877 for group A are significantly different from the observed rates of 10/20000 for group B assuming Poisson counts. The poisson.test function in the stats package

Hi all, I have just uploaded the hbim package. This package does calculations related to the Hill/Bliss independence model as applied to mulitcomponent vaccines. Specifically, the package caculates expected relative risk and proportion protected assuming normally distributed log10 transformed antibody dose for a several component vaccine. It uses Hill models for each component which are combined

Hi all, I have just uploaded the hbim package. This package does calculations related to the Hill/Bliss independence model as applied to mulitcomponent vaccines. Specifically, the package caculates expected relative risk and proportion protected assuming normally distributed log10 transformed antibody dose for a several component vaccine. It uses Hill models for each component which are combined

Hi friends, I get different values for McNemar's test in R and SPSS. Which one should i rely on when the p values differ. I came across this problem when i started learning R and seriously give up on SPSS or any other proprietary software. Thank u in advance Output in SPSS follows *Crosstab* hsc Total ABN NE ABN tvs ABN Count 40 3 43 Row % 93.0% 7.0%