similar to: permutated p values vs. normal p values

Dear all, I fit independent GLMs for a 2x2 factorial problem on the data matrix of size 9500 x 12 (genes x arrays) and get 9500 observed t-values using the apply() function. Now, I wish to get the permutated p-values. Therefore I random sample the class labels and perform the glm fitting to get the t-values from which I can get the p-values. This is done using a for() loop. Is there a more

Given an x-vector with, say, 3 elements, I would like to compute the following vector of permutated products (1-x1)*(1-x2)*(1-x3) (1-x1)*(1-x2)*x3 (1-x1)*x2*(1-x3) x1*(1-x2)*(1-x3) (1-x1)*x2*x3 x1*(1-x2)*x3 x1*x2*(1-x3) x1*x2*x3 Now, I already have the correctly sorted matrix of permutations! So, the input looks something like: #input x<-c(0.3,0.1,0.2) Nx<-length(x) Ncomb<-2^Nx

Hi, May be this helps: ?set.seed(24) ?mydata4<- as.data.frame(matrix(sample(1:100,10*38,replace=TRUE),ncol=38)) ?dim(mydata4) #[1] 10 38 ?library(matrixStats) res<-do.call(cbind,lapply(1:400, function(i) {permutation<-sample(mydata4); (rowMeans(permutation[,1:27])-rowMeans(permutation[,28:38]))/(rowSds(permutation[,1:27])+rowSds(permutation[,28:38]))} )) ?dim(res) #[1]? 10 400 A.K.

I am perfectly aware that this question is not an R question, at least not yet, but I have not succeeded in finding what I want in other ways, so ... What I am looking for are two algorithms, preferabley in Pascal, but other languages may do. For (a) systematic (complete) permutations for grouped data with unequal group sizes, and (b) random permutations for the same kind of data. I know

Dear all, Does anyone knows why the results of a BIOENV (PRIMER v. 6.1.15) are diferent of the bioenv() + mantel() in vegan? Not the spearman correlation, indeed the pseudo-p value. I know that the approach bioenv() + mantel() is biased. So, how the BIOENV (PRIMER) ends with larger p values (permutated). Acctualy how the permutation test in BIOENV (PRIMER) is conducted. The user guide does not

Hi, I want to permutate the following matrix and replace permutated columns. Is it possible to control the number of columns permutated. Let's say I only want to permute two columns. Can i do that with the sample method or should i any bootstrapping method ?? I'm not sure this is the best statisticaly way of doing it...?? So the idea behind is to ramdonly generate 1000 permutated

Hi, I have a microarray dataset from Agilent chips. The data were really log ratio between test samples and a universal reference RNA. Because of the nature of log ratios, coefficient of variation (CV) doesn't really apply to this kind of data due to the fact that mean of log ratio is very close to 0. What kind of measurements would people use to measure the dispersion so that I can compare

Dear List, I am new to R and find it very powerful. I would like to compute the permutational p-value for paired data using Kolmogorov-Smirnov, but the built-in ks.test does not have this option, unlike the t.test which has a paired=TRUE flag. Has someone written a library or a routine that does this? Alternatively, if someone could show me how to do pair-wise permutations in R, then I can

Hi, I'm running a tutorial ("Meta-analyses of data from two (or more) microarray data sets"), which use wgcna package. I have an error in the function modulePreservation (it is below). I'm using R2.13 Can you help me? Do you know, what is happens? Thanks Raquel multiExpr = list(A = list(data=t(badea)),B = list(data=t(mayo))) # two independent datasets (dim = 13447 x 36) mp =

Hi, I am having trouble understanding how to approach a simulation: I have a sample of n=250 from a population of N=2,000 individuals, and I would like to use either permutation test or bootstrap to test whether this particular sample is significantly different from the values of any other random samples of the same population. I thought I needed to take random samples (but I am not sure how

Hello, I am trying to explore the use of random forests for classification and am certain about the interpretation of the importance measurements. When having the option "importance = T" in the randomForest call, the resulting 'importance' element matrix has four columns with the following headings: 0 - mean raw importance score of variable x for class 0 (where

Hallo, > fit12<-lmFit(qrg[,1:2]) > t12<-toptable(fit12,adjust="fdr",number=25,genelist=qrg$genes[,1]) > t12 ID logFC t P.Value adj.P.Val B 522 PLAU_OP -6.836144 -8.420414 5.589416e-05 0.01212520 2.054965 1555 CD44_WIZ -6.569622 -8.227938 6.510169e-05 0.01212520 1.944046 Can anyone tell me what the difference is between P.Value

Dear R users, There is a error message when I run the following code. It is used to load microarray data and use the top 1000 genes for training svm to classify test set . > library(e1071) Loading required package: class > f=read.table("F:\\lab\\ microarray analysis\\VEH LPS\\exprs.txt",

Hello, here is the problem: I want to demonstrate that, on average, the Pearson's correlations of a specified subset of genes from a huge list (>18,000 columns) are higher than any randomly chosen subset of that list. I would therefore like to do a number of tests between that specified subset and randomly chosen ones from the "mother" list. How could I do that? What would be

Hi: I have a small problem that I'm not quite sure how to figure out. I've generated some randomized (permutated) data which consists of nrows=3 by ncols=165 (see below) per element (3 x 165 = 495 sub-elements). [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]....... [1,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [2,]

Hi *, is there a way to obtain some kind of p-value for a model fitted with RMA using the lmodel2 package? I know that p-values are discussed and criticized a lot and as you can image from my question I'm not very much of a statistican (only writing my bachelor thesis). As fare as I understood the confidence interval statistic correctly, a coefficient is regarded as statistically significant

Hello R-users, I am looking for an elegant way to calculate p-values for each row of a data frame. My situation is as follows: I have a gene expression results from a microarray with 64 samples looking at 25626 genes. The results are in a data frame with the dimensions 64 by 25626 I want to create a volcano plot of difference of means vs. ?log(10) of the p-values, comparing normal samples to

I am looking for an algorithm (written in R (preferably) or C, but even pseudo-code in a text book maybe fine) to determine the sign of a permutation. What is that? Well, a permutation is either even or odd, the sign is +1 or -1, respectively, see, e.g., http://en.wikipedia.org/wiki/Signature_of_a_permutation which also says >> In practice, in order to determine whether a given

> On May 22, 2018, at 10:57 PM, John <miaojpm at gmail.com> wrote: > > Thanks, David. > I got the answer from the web. > Is there any easy way to permute a set (e.g., a set of characters) by the permutation it returns? Thanks, > > > > x <- c(10,7,4,3,8,2) > > sort(x, index.return=TRUE) > $x > [1] 2 3 4 7 8 10 > > $ix > [1] 6 4 3 2

Thanks, David. I got the answer from the web. Is there any easy way to permute a set (e.g., a set of characters) by the permutation it returns? Thanks, > x <- c(10,7,4,3,8,2) > sort(x, index.return=TRUE) $x [1] 2 3 4 7 8 10 $ix [1] 6 4 3 2 5 1 2018-05-23 10:49 GMT+08:00 David Winsemius <dwinsemius at comcast.net>: > > > > On May 22, 2018, at 10:06 PM, John