similar to: power.t.test formula

In the summary of the output of arma, there's a number Pr(>|t|), however, I don't know what is its meaning - at least, it doesn't _seem_ to be a Student's t distribution. Reproducible test case: x <- c(0.5, sin(1:9)) reg <- arma(x, c(1,0)) summary(reg) <output> Call: arma(x = x, order = c(1, 0)) Model: ARMA(1,0) Residuals: Min 1Q Median 3Q

Hello, I use t.test for normal distributed and wilcox.test for non-normal distributed samples. It is easy to write a function for t.test that calculates the effect size, because all parts of the formula are available from the t.test result: r = sqrt(t*t / (t*t + df)) However, for Wilcoxon tests, the formula for effect sizes is: r = Z / sqrt(N) I wonder how I can calculate the Z-score in R for

Hi, how can I solve a problem without the function t.test??? for example: x<-(1,3,5,7) y<-(2,4,6) t.test(x,y,alternative="less",paired=FALSE,var.equal=TRUE,conf.level=0.95) -- View this message in context: http://www.nabble.com/t.test-tf4034225.html#a11460445 Sent from the R help mailing list archive at Nabble.com.

I have been attempting some basic power calculations using R and I am not getting the results I expect. I had a homework assignment in SAS, but I want to learn R as well, so I was attempting to reproduce my result. (No one else in the class is doing R, so there's no need to obsfucate the answer, the SAS code is what I get my grade for.) The code I am using is: # You assume that the

Dear R People: Here is a small data frame and two particular formulas: > test.df y x 1 -0.9261650 1 2 1.5702700 2 3 0.1673920 3 4 0.7893085 4 5 0.3576875 5 6 -1.4620915 6 7 -0.5506215 7 8 -0.3480292 8 9 -1.2344036 9 10 0.8502660 10 > summary(lm(exp(y)~x)) Call: lm(formula = exp(y) ~ x) Residuals: Min 1Q Median 3Q Max -1.6360 -0.6435

Hi, I am trying to sort out a discrepancy between power calculations results between me and another statistician. I use R but I am not sure what she uses. It is on the proportions test and so I have been using pwr.prop.test. I think I have tracked the problem down to pwr.prop.test not using the continuity correction for the test (I did this by using the java applet from

Hi I have a vector x of length n. I am interested in x[1] being different from the other observations (ie x[-1]). My null hypothesis is that x[1] is drawn from a Gaussian distribution of the same mean as observations x[-1], which are assumed to be iid Gaussian. The (unknown) variance of x[1] is assumed to be the same as the variance of x[-1]. This should be an unpaired t-test. But

Hi list,Could you please help me to explain the following error messages with 't.test' in R Unix 2.1.1? I don't see it in R under Windows (R 2.3.0) or Unix (R2.3.1). Is it really due to the different R versions?Thanks,...TaoUnix session: (R.2.1.1)========================> R.version _ platform x86_64-unknown-linux-gnuarch x86_64 os

Dear all, I found a discrepancy while performing a power calculation for a two sample t-test in R and S-PLUS, respectively. For given values of sample number (5 each), sd (0.2) , significance level (0.01), and a desired power (80%) I looked for the difference in means. These values differ: 0.5488882 in R and 0.4322771 in S-PLUS (see dump below). Did I overlook any detail or confuse some

What would be the R formulae for a two-sided test? I have a formula for a one-sided test: powertest <- function(a,m0,m1,n,s){ t1 = -qnorm(1-a) num = abs(m0-m1) * sqrt(n) t2 = num/s pow = pnorm(t1 + t2) } Would you pls let me know if you know of? Thank you, ej

Dear friends, regression towards the mean is interesting in medical circles, and a very recent paper (The American Statistician November 2007;61:302-307 by Krause and Pinheiro) treats it at length. An initial example specifies (p 303): "Consider the following example: we draw 100 samples from a bivariate Normal distribution with X0~N(0,1), X1~N(0,1) and cov(X0,X1)=0.7, We then calculate

When I do t.test on two distributions (see example below), it outputs numerous data about the t.test. What I'd like to do is individually capture some of this data and assign it to other variables. However, I am unable to find anything in the help section. In the example below, the t value is -4.0441 and the p-value is 0.006771 How can I assign these values to two variables, let's

Hallo! I just started working with R to do the statistical analyses for my diploma thesis. I got two sets of data. Both contain repeated measures. One has only one within-subject factor with four levels. The other has one within-subject factor with two levels and one between-factor with two levels. I want to compute a Mauchly test for both sets and a Levene test for the second set. I

Dear people, I've a problem in doing a power calculation. In Fryer and Nicholson (1993), ICES J. mar. Sci. 50: 161-168 page 164 an example is given with the following characteristics T=5, points in time R=5, replicates Var.within=0.1 q=10, a 10% increase per year The degrees of freedom for the test are calculated as Vl=T*R-2=23 and the non-centrality parameter Dl=4.54. Using this they get a

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)))

Hello, I was wondering if someone knows the formula used by the function lm to compute the t-values. I am trying to implement a linear regression myself. Assuming that I have K variables, and N observations, the formula I am using is: For the k-th variable, t-value= b_k/sigma_k With b_k is the coefficient for the k-th variable, and sigma_k =(t(x) x )^(-1) _kk is its standard deviation.

Say I have a formula Y ~ 1 + X, where X is a categorical variable. A previous thread showed how to evaluate this model using the mle package from "stats4" (see below). But, the user had to create the data matrix, X, including the column of one's for the regression constant. Is there a way to nest the linear formula in the code below, so the data matrix doesn't explicitly

Power calculations two sample test for proportions is very useful. Is there a way however, to get away from the two samples being of the same size. What would happen if one had n=15 in the one sample and n=45 in the other sample. Farrel Buchinsky, MD Pediatric Otolaryngologist Allegheny General Hospital Pittsburgh, PA **********************************************************************

Your understanding isn't similar to mine. Mine says robust/resistant methods are for data with heavy tails, not heteroscedasticity. The common ways to approach heteroscedasticity are transformation and weighting. The first is easy and usually quite effective for dose-response data. The second is not much harder. Both can be done in R with nls(). Andy From: Quin Wills > > I am

To R-helpers, R offers the polyroot function for solving mentioned equations iteratively. However, Dr Math and Mathworld (and other places) show in detail how to solve mentioned equations non-iteratively. Do implementations for R that are non-iterative and that solve mentioned equations exists? Regards, Mads Jeppe