similar to: {10,20,30}>={25,30,15}

Hello. How can I compute the Bootstrap p-Value for a one- and two sided test, when I have a bootstrap sample of a statistic of 1000 for example? My hypothesis are for example: 1. Two-Sided: H0: mean=0 vs. H1: mean!=0 2. One Sided: H0: mean>=0 vs. H1: mean<0 I hope you can help me Thanks in advance Regards, Andreas

Dear all, I have a multivariate dataset containing 100,000 or more points. I want find the p-value for the dataset of points coming from a particular multivariate normal distribution With mean vector u Covariance matrix s2 So H0: points ~ MVN( u, s2) H1: points not ~ MVN( u, s2) How do I find the p-value in R? To me this is a likelihood ratio test problem. In H0 the parameters are

Dear R users I am using KS test to compare two different distribution for the same variable (temperature) for two different time periods. H0: the two distributions are equal H1: the two distributions are different ks.test (temp12, temp22) Two-sample Kolmogorov-Smirnov test data: temp12 and temp22 D = 0.2047, p-value < 2.2e-16 alternative hypothesis: two-sided Warning message: In

Hi Dear all, I have some gene expression data samples from different tissue types ----------------------------------------------- - 120 samples from blood (B) - 20 samples from Liver (L) - 15 samples from Kidney (K) - 6 samples from heart (H) ----------------------------------------------- All the samples are from different individuals, so there are in total 161 individuals from which the DNA was

Hi everybody, somehow i dont get the shapiro wilk test for normality. i just can?t find what the H0 is . i tried : shapiro.test(rnorm(5000)) Shapiro-Wilk normality test data: rnorm(5000) W = 0.9997, p-value = 0.6205 If normality is the H0, the test says it?s probably not normal, doesn ?t it ? 5000 is the biggest n allowed by the test... are there any other test ? ( i know qqnorm

Hi, I am running glm logit regressions with R and I would like to test a linear combination of coefficients (H0: beta1=beta2 against H1: beta1<>beta2). Is there a package for such a test or how can I perform it otherwise (perhaps with logLik() ???)? Additionally I was wondering if there was no routine to calculate pseudo R2s for logit regressions. Currently I am calculating the pseudo R2

Hi all, I am wondering whether there is a small bug in the binom.test function of the ctest library (I'm using R 1.6.0 on windows 2000, but Splus 2000 seems to have the same behaviour). Or perhaps I've misunderstood something. the command binom.test(11,100,p=0.1) and binom.test(9,100,p=0.1) give different p-values (see below). As 9 and 11 are equidistant from 10, the mean of the

I estimate two competing simple regression models, A and B where the LHS is the same in both cases but the predictor is different ( I handle the intercept issue based on other postings I have seen ). I estimate the two models on a weekly basis over 24 weeks. So, I end up with 24 RSquaredAs and 24 RsquaredBs, so essentally 2 time series of Rsquareds. This doesn't have to be necessarily thought

Dear R, I am using this R version:R version 2.11.1 (2010-05-31)(Cran Mirror Berlin) It seems to me, that R constructs a wrong confidence intervall if you try to get a one sided t-test. If the true mean is 1 and my alternative hypothesis (H1) says that mu is smaller ("less")than zero the conf. intervall should reach +∞ and not -∞ if it is constructed for the H0 saying that mu is

Hi I was wondering if there is a permutation test available in R for linear models with continuous dependent covariates. I want to do a test like the one shown here. bmi<-rnorm(100,25) x<-c(rep(0,75),rep(1,25)) y<-rnorm(100)+bmi^(1/2)+rnorm(100,2)*x+bmi*x H0<-lm(y~1+x+bmi) H1<-lm(y~1+x+bmi+x*bmi) anova(H0,H1) summary(lm(y~1+x+bmi)) But I want to use permutation testing to

Hi, I had a question about specifying the Null hypothesis in a significance test. Advance apologies if this has already been asked previously or is a naive question. I have two samples A and B, and I want to test whether A and B come from the same distribution. The default Null hypothesis would be H0: A=B But since I am trying to prove that A and B indeed come from the same distribution, I think

Hi Let's assume, we have a multiple linear regression, such as the one using the Scottish hills data (MASS, data(hills)): one dependent variable: time two independent var (metric): dist, climb if I am interested, after (!) fitting a lm: my. lm <- lm(time ~ dist + climb, data = hills) in the equivalence (or non-equivalence) of the two predictors "dist" and

On (Mon) 28 Jul 2014 [15:32:42], Amos Kong wrote: > QEMU commandline: > > ./x86_64-softmmu/qemu-system-x86_64 --enable-kvm -m 2000 -drive file=/images/nolvm.qcow2 --kernel /home/devel/linux/arch/x86/boot/bzImage -append "ro root=/dev/sda1 console=ttyS0,115200" -monitor unix:/tmp/m,nowait,server -device virtio-net-pci,netdev=h0,vectors=17,mq=on,id=n0 -netdev

On (Mon) 28 Jul 2014 [15:32:42], Amos Kong wrote: > QEMU commandline: > > ./x86_64-softmmu/qemu-system-x86_64 --enable-kvm -m 2000 -drive file=/images/nolvm.qcow2 --kernel /home/devel/linux/arch/x86/boot/bzImage -append "ro root=/dev/sda1 console=ttyS0,115200" -monitor unix:/tmp/m,nowait,server -device virtio-net-pci,netdev=h0,vectors=17,mq=on,id=n0 -netdev

Hi all, does anyone know how to automatically update starting values in R? I' m fitting multiple nonlinear models and would like to know how I can update starting values without having to type them in. thank all --- On Fri, 12/26/08, r-help-request@r-project.org <r-help-request@r-project.org> wrote: From: r-help-request@r-project.org <r-help-request@r-project.org> Subject:

hi there, i have a few questions about the correct interpretation of a paired t-test. (i don't think that this matters, but I'm using R 2.10.1 on Windows). to my questions: i've been using a lot of time about this minor concerns now and I hope you can help me... I use one- and two-sided t-tests. My questons are on one side about how R uses the hypothesis' and the second one

On Mon, Jul 28, 2014 at 01:25:14PM +0530, Amit Shah wrote: > On (Mon) 28 Jul 2014 [15:32:42], Amos Kong wrote: > > QEMU commandline: > > > > ./x86_64-softmmu/qemu-system-x86_64 --enable-kvm -m 2000 -drive file=/images/nolvm.qcow2 --kernel /home/devel/linux/arch/x86/boot/bzImage -append "ro root=/dev/sda1 console=ttyS0,115200" -monitor unix:/tmp/m,nowait,server

On Mon, Jul 28, 2014 at 01:25:14PM +0530, Amit Shah wrote: > On (Mon) 28 Jul 2014 [15:32:42], Amos Kong wrote: > > QEMU commandline: > > > > ./x86_64-softmmu/qemu-system-x86_64 --enable-kvm -m 2000 -drive file=/images/nolvm.qcow2 --kernel /home/devel/linux/arch/x86/boot/bzImage -append "ro root=/dev/sda1 console=ttyS0,115200" -monitor unix:/tmp/m,nowait,server

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

Hi All This is a general stats problem that I am dealing with using R, so any help is greater appreciated. I have two lognormal distributions with means M1 and M2. If we have: H0: log(M1/M2)=0 H1: log(M1/M2) !=0 equivalent to log(M1/M2)=log(1+P) where P<0 or P>0. If we calculate the power for a value of P=0.1 or P=-0.1 (i.e. a 10% difference) and say assume SE{log(M1/M2)}=0.05, and