similar to: nonparametric significance test for one sample

Hello, I got two sets of data x=(124738, 128233, 85901, 33806, ...) y=(25292, 21877, 45498, 63973, ....) When I did a t test, I got two tail p-value = 0.117, which is not significantly different. If I changed x, y to log scale, and re-do the t test, I got two tail p-value = 0.042, which is significantly different. Now I got confused which one is correct. Any help would be very appreciated.

Hi all, I have a distribution, and take a sample of it. Then I compare that sample with the mean of the population like here in "Wilcoxon signed rank test with continuity correction": > wilcox.test(Sample,mu=mean(All), alt="two.sided") Wilcoxon signed rank test with continuity correction data: AlphaNoteOnsetDists V = 63855, p-value = 0.0002093 alternative hypothesis:

Hello! I am trying to use lm to do a simple regression but on a batch of different files. Each file has different column names. I know the first column is the dependent variable and all the rest are explanatory variables. The column names are in the first line of the file. But it seems lm() requires I know the variable names in advance? Is there a way to dynamically read in variable names

When I use wilcox.test, I get vastly different p-values than the problems from Statistics textbooks. For example: The following problem comes from "Applied Statistics and Probability for Engineers", 2nd Edition, by D. C. Montgomery. Page736, problem 14.7. The problem is to compare the sample data with a population median of 8.5. The book answer is p = 0.25, wilcox.test answer is p =

Hi useRs, can a variance test for 2 non-normal samples be tested in R? Also, thus far I have not been able to find the Friedman two way analysis of variance. For normal r.v., the var.test is available, but are there any tests available for non-normal samples? Thanks! Bernd

I have a dataset of 10000 records which I want to use to compare two prediction models. I split the records into test dataset (size = ntest) and training dataset (size = ntrain). Then I run the two models. Now I want to shuffle the data and rerun the models. I want many shuffles. I know that the following command sample ((1:10000), ntrain) can pick ntrain numbers from 1 to 10000. Then I just

Hi, Anybody used treenet here? I downloaded a demo but don't know how to start with. Does R has something like treenet? Thanks, [[alternative HTML version deleted]]

Hi, Sorry I have to bother you a question. I have a file with each line like this: 6.5500000000000000e+004 2.8235000000000001e+000 3.2000000000000000e+001 1.1580000000000000e+003 2.4400000000000000e+002 5.9800000000000000e+002 2.2700000000000000e+002 3.9030000000000001e+001 -1.2137000000000000e+002 However, I use read.table, it cannot read it correctly. It read in 18 variables instead of

Dear list: Thanks a lot for help. I have a question and I could not find clear answers easily. When we do logistic regression for one type of events of interest as a proportion of a broader types of events, does the logistic regression assume that the number of whole types of events should be independent with the number of type of interest? For example, if one type of events and the whole type

I have a question concerning the Wilcoxon signed-rank test, and specifically, which R subroutine I should use for my particular dataset. There are three different commands in R (that I'm aware of) that calculate the Wilcoxon signed-rank test; wilcox.test, wilcox.exact, and wilcoxsign_test. When I run the three commands on the same dataset, I get different p-values. I'm hoping that

My interpretation of the relation between 1-way ANOVA and Wilcoxon's test (wilcox.test() in R) is the following. 1-way ANOVA is to test if two or multiple distributions are the same, assuming all the distributions are normal and have equal variances. Wilcoxon's test is to test two distributions are the same without assuming what their distributions are. In this sense, I'm wondering

Hi, I recently ran into the problem that I needed a Siegel-Tukey test for equal variability based on ranks. Maybe there is a package that has it implemented, but I could not find it. So I programmed an R function to do it. The Siegel-Tukey test requires to recode the ranks so that they express variability rather than ascending order. This is essentially what the code further below does. After the

For some small sample sizes I would like to exemplify the combinatorics underlying certain nonparametric tests like Mann-Whitney-Wilcoxon, Kruskal-Wallis and Spearman's rank correlation. I have written a function all.perm which delivers all permutations of 1,2,...,n and which works reasonably well. This can be used to generate P(R=r) of Spearman's rank correlation:

Dear Sirs, My name is Marcelo Grimaldi and I work for GE Lighting Brazil in Six Sigma Program. In order to accomplish a statistical project I need to generate exact tables for the Kruskal-Wallis statistic ("Three or More Populations Location Test") and I wonder if R could help me. In the past I have heard of generating tables for Wilcoxon statistic using R. Could you help me ? Thank

Por si alguno pudiera ayudarme. Al realizar el t.test para una muestra, junto con el valor de t y el p-valor, la función proporciona la estimación de la media y su INTERVALO DE CONFIANZA. Desde el punto de vista de la estadística de rangos esto se puede hacer mediante: > iris$MEDIANA <- with(iris, 2.95) > median(iris$Sepal.Width - iris$MEDIANA, na.rm=TRUE) # median difference [1]

how do I check the rank of a matrix ? say A= 1 0 0 0 1 0 then rank(A)=2 what is this function? thanks I did try help.search("rank"), but all the returned help information seem irrelevant to what I want. I would like to know how people search for help information like this. rank(base) Sample Ranks SignRank(stats) Distribution of the

Hello Everyone,   I'm trying to conduct a couple of power analyses and was hoping someone might be able to help. I want to estimate the sample size that would be necessary to adequately power a couple of non-inferiority tests. The first would be a log-rank test and the second would be a Wilcoxon rank-sum test. I want to be able to determine the sample size that would be necessary to test for a

Hi, R allows me to run a one sample Wilcoxon test like this: wilcox.test(c(1,3.5,2.1,4,1.5,5), mu=2, exact=TRUE) The function 'wilcoxsign_test' from the package 'coin' should (I suppose) be able to calculate exact p values even if there are ties in the ranks. However, I couldn't find information on how to run a one sample test using 'wilcoxsign_test' like in the

Given this example #start code a<-c(0,70,50,100,70,650,1300,6900,1780,4930,1120,700,190,940, 760,100,300,36270,5610,249680,1760,4040,164890,17230,75140,1870,22380,5890,2430) b<-c(0,0,10,30,50,440,1000,140,70,90,60,60,20,90,180,30,90, 3220,490,20790,290,740,5350,940,3910,0,640,850,260) wilcox.test(a, b, paired=FALSE) #sum of rank for first sample sum.rank.a <-

Sorry if this subject has been already dealt here. Which are some common tests for nonparametric k-way ANOVA? I have read about Kruskal-Wallis test as a kind of nonparametric one-way ANOVA, but I have not found anything about a general-setting (I mean k-way) nonparametric ANOVA. Can you recommend me a good R package (or other reliable software) for that? Looking forward to your answers, --