similar to: Permutation columns or boostrapping

Hi, I'm very new to working with sparse matrices and would like to know how I can column permute a sparse matrix. Here is a small example: > M1 <- > spMatrix(nrow=5,ncol=6,i=sample(5,15,replace=TRUE),j=sample(6,15,replace=TRUE),x=round_any(rnorm(15,2),0.001)) > M1 5 x 6 sparse Matrix of class "dgTMatrix" [1,] 2.983 . 1.656 5.003 . . [2,] .

Dear R users, I am trying to perform a hypothesis test on a marked point pattern. I would like to calculate the mean of the absolute value of the difference of marks between nearest neigbours, randomize the marks among points, then calculate this mean again. Ideally, I would test whether random mean values smaller than the observed mean value occur less than 5% of the time. I suppose 1000

R users, Is it possible to do 'exact' permutation tests in R? If I run a perm.test or permutation.test.discrete on a data set that has, for example, 720 possible permutations, will that test permute ALL of those possible permutations, or will it just permute 720 random permutations (thus allowing some possibilities to repeat and some not to be permuted at all)? I'm a little confused

Insightful are pleased to announce we are now taking bookings for our latest course on "Bootstrap methods and permutation tests" in the UK on 23rd - 24th October. This course will focus in particular on two resampling methods, bootstrapping and permutation tests, which have been applied successfully to areas of statistical modelling where "traditional" standard errors,

> 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

Hi, I am performing Cox proportional hazards regression on a microarray dataset with 15000 genes. The p values generated from the Cox regression (based on normal distribution of large sample theory) showed only 2 genes have a p value less than 0.05. However, when I did a permutation on the dataset to obtained permutated p values, and it turned out about 750 genes had a permutated p value less than

Hello, Like David said, what you are trying to do with sort() can be done with order() in a much easier way. First, your code x <- sort(c("bc","ac","dd"), index.return=TRUE) Now, with function order() i <- order(c("bc", "ac", "dd")) y <- c("D","E", "F")[i] y #[1] "E" "D"

Hello, I'm trying to permute a vector of positive integers > 0 with the constraint that each element must be <= twice the element before it (i.e. for some vector x, x[i] <= 2*x[i-1]), assuming the "0th" element is 1. Hence the first element of the vector must always be 1 or 2 (by assuming the "0th" element is 1). Similarly, the 2nd must always be below/= 4, the

Hi, Is there any way to find the permutation function of the sorting and to apply the function (or its inverse) elsewhere? For example, the following permutation function from the sorting in the matrix form is c(1,2,3), c(2,1,3) > sort(c("bc","ac","dd")) [1] "ac" "bc" "dd" I try to find it in the permutations/permute

Hello List and thanks in advance for all of your help, I am trying implement a permutation test of a multinomial logistic regression ('multinom' within the nnet package). In the end I want to compare the parameter estimate from my data to the distribution of randomized parameter estimates. I have figured out how to permute my dependent variable (MNNUM) x number of times, apply

Hello All, Suppose that I have a sequence of letters (e.g., A..Z). I want to permute the sequence to generate random sequences, such that the local density of any letter with a window (say of length L) doesn't change much before and after permutation. One way that I can thing of is to require that the position of any letter in the original sequence and the position of the same letter in the

> On May 22, 2018, at 10:06 PM, John <miaojpm at gmail.com> wrote: > > Hi, > > Is there any way to find the permutation function of the sorting and to > apply the function (or its inverse) elsewhere? > > For example, the following permutation function from the sorting in the > matrix form is > c(1,2,3), c(2,1,3) > >>

> sort(c("bc","ac","dd"), index.return=TRUE) $x [1] "ac" "bc" "dd" $ix [1] 2 1 3 We have the permutation, namely 1-->2, 2-->1, 3-->3. How can I apply the permutation function to a new set c("D","E", "F")? so that the result is c("E","D", "F"). 2018-05-23 11:06

Hello, I'm using R code that includes a residual permutation that was written as a supplement to the paper: Turner et al. 2010. A general hypothesis-testing framework for stable isotopes ratios in ecological studies. Ecology 91:2227-2233. The supplemental code is available at: http://www.esapubs.org/archive/ecol/E091/157/suppl-1.htm When I execute the function, no warnings are given

Hi everyone, I am somewhat new to R and I am trying to write a permutation function such that it inputs a character vector and from an arbitrary length "n" which is the length of the combinations for the character vector. I know there are R packages for permutation but this is for an assignment. So far this is what I have: alphabet =

Hallo Is there an elegant way to do the following: Dataset consists of 2 variables: var1: some measurements, and var2: a grouping variable with two values, 1 and 2. There are (say) 10 measurements from group 1 and 15 measurements from group 2. The idea is to study the permutation distribution of mean(group 1) * mean(group2). One way would be to permute 1s and 2s and select the corresponding

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

Hello everyone, I am looking at the joint spatial distribution of 2 kinds of organisms (estimated on a grid of points) and want to test for significant association or dissociation. My first question is: do you know a nice technique to do that, considering that I have a limited number of points (36) but that they are repeated (4 times)? I did GLMs to test for correlations between the

Hello, I have what I suspect might be an easy problem but I am new to R and stumped. I have a data set that looks something like this b<-c(2,3,4,5,6,7,8,9) x<-c(2,3,4,5,6,7,8,9) y<-c(9,8,7,6,5,4,3,2) z<-c(9,8,7,6,1,2,3,4) data<-cbind(x,y,z) row.names(data)<-c('a','b','c','d','e','f','g','h') which gives: x y z