similar to: requesting a mentor for R development

As a very new user of R, and a user of S with very limited experience, I thought I would start humbly with a simple t.test. The response to the command sequence sample <- scan() 4 5 6 7 8 9 t.test(sample,mu=20) rather surprised me, because of the idiosyncratic choice of a confidence interval for the mean of the population from which the sample was drawn. I asked my colleague Bob Henery

I wish the t.test function in stats would return the standard error. It would be nicer for students if R simply reported the standard error used to calculate the t value. I trolled for this in r-help and got no answers, which I interpreted to mean that this is boring but possibly not wrong. Hopefully. I believe only simple changes are needed. In the source code src/library/stats/t.test.R file:

Hello, I wuold like to propose my modifications of the original cor.test to you : I tried to calcolate the correct p-value for Spearman and Kendall's test with ties. Let me know what you think. Thanks you for your time. Antonietta di Salvatore test <- function(x, ...) UseMethod("test") test.default <- function(x, y, alternative = c("two.sided",

Greetings! I want to have the coefficients that R uses in shapiro.test() for the Shapiro-Wilk test for a prticular sample size, i.e. the a[i] in W = Sum(a[i]*x[i])/(Sum(x[i] - mean(x))^2) (where the x[i] are sorted). Two questions: Q1: Is there a readymade R function from which I can extract these? Q2: I was wondering if I might be able to modify the code for the function shapiro.test() so

Full_Name: Wei Xu Version: 1.5.1 OS: WindowsME Submission from: (NULL) (63.215.238.92) The P-value for a two.sided test is not consistent with the confidence interval. For example, P-value=0.1372, but the 95% CI doesn't include the H0 value(1). > x [1] 0.80 0.83 1.89 1.04 1.45 1.38 1.91 1.64 0.73 1.46 > y [1] 1.15 0.88 0.90 0.74 1.21 >

Full_Name: Dirk Koschuetzki Version: 2.1.1 OS: source code Submission from: (NULL) (194.94.136.34) Hello, >From the source code (R-2.1.1, file: .../R-2.1.1/src/library/stats/R/) ****************************** cor.test.default <- function(x, y, alternative = c("two.sided", "less", "greater"), method = c("pearson", "kendall",

Dear useRs, I was searching CRAN for implementation of kurtosis and skewness tests, and found that there is some kind of lack on it. So, I have written three functions: 1. Anscombe-Glynn test for kurtosis 2. Bonett-Seier test based on Geary's kurtosis (which is not widely known, but I was inspired by original paper describing it, found coincidentally in Elsevier database) 3.

Hello All, I was wondering how I can get the overall Pearson chi^2 test of model fit with its df and p value in the LTM package for the 2PL models. Thanks, -- Davood Tofighi Department of Psychology Arizona State University [[alternative HTML version deleted]]

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# Why does expressing one function require(ctest) t.test # return only function (x, ...) UseMethod("t.test") <environment: namespace:ctest> # but expressing another function shapiro.test # returns more complete code? function (x) { DNAME <- deparse(substitute(x)) x <- sort(x[complete.cases(x)]) n <- length(x) if (n < 3 || n > 5000)

Dear R Users, Is the p-value reported in a two-tailed binomial exact test in error or is it a feature? If it is a feature, could someone provide a reference for its two-tailed p-value computations? Using Blaker's (2000 - Canad. J. Statist 28: 783-798) approach,the p-value is the minimum of the two-tailed probabilities $P \left(Y\geq y_{obs}\right)$ and $P\left(Y\leq y_{obs}\right)$

Hi all .. there is no doubt a simple answer to this, but it eludes me. In the first session below ( with jarque.bera.test) you will see that p.value prints with a name of X-squared . This is easily fixed by changing the source to assign a more appropriate name - no name is assigned in the source listing below (the original source code of jarque.bera.test() from tseries).. but what I

Hi. I'd like to do a t-test to compare the Delta values of items with Crit=1 with Delta values of items with Crit=0. What is the t.test syntax? It should produce a result like this below (I can't get in touch with the person who originally did this for me) Welch Two Sample t-test data: t1$Delta by Crit t = -3.4105, df = 8.674, p-value = 0.008173 alternative hypothesis: true

Hi Now a more theoretical question. I have two correlation matrices - one of a set of variables under a particular condition, the other of the same set of variables under a different condition. Is there a statistical test I can use to see if these correlation matrices are "different"? Thanks Mick

This is an OpenPGP/MIME signed message (RFC 2440 and 3156) --------------enig5A700CC15DEABD01445A1B70 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable Hi, I have modified the code originally posted to include capability for arbitrary weightings for the rows and columns (which apply in the nominal-ordinal and the ordinal-ordinal cases). The code is pasted

Hello, I'm looking to calculate a 95% confidence interval about my estimate for a sample's weighted mean, where the calculated confidence interval would equal the t-test confidence interval of the sample in the case when all of the weights are equal. My initial thought was to simply implement a modified version of the t-test function but substituting the weighted variance and mean for the

Hi, Does anyone know if the D'agostino test is available with R ? Alex

I've written a g.test() aka log-likelihood ratio test function for my opwn use. It's something I've seen requested (and looked to find myself) on this list a few times. It has the same basic syntax as chisq.test(). It does both goodness of fit tests and tests of independence. Yates' and Williams' corrections are implemented. I've put some examples from Sokal & Rohlf

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Hello! I am running a very simple mini Monte-Carlo below using the function tstatistic (right below this sentence): tstatistic = function(x,y){ m=length(x) n=length(y) sp=sqrt( ((m-1)*sd(x)^2 + (n-1)*sd(y)^2)/(m+n-2) ) t=(mean(x)-mean(y))/(sp*sqrt(1/m+1/n)) return(t) } alpha=.1; m=10; n=10 # sets alpha, m, n - for run 1 N=10000 # sets the number of simulations n.reject=0 # counter of num.