search for: nperms

I've been attempting to understand the one-sample run test for randomness. I've found run.test{tseries} and run.test{lawstat}. Both use a large sample approximation for distribution of the total number of runs in a sample of n1 observations of one type and n2 observations of another type. I've been unable to find R code to generate the exact distribution and would like to see how

...s] key <mode [modes...]>", progname); #endif } @@ -78,10 +80,61 @@ do_rm(char *path, struct xs_handle *xsh, } #endif +#if defined(CLIENT_chmod) +#define PATH_SEP ''/'' +#define MAX_PATH_LEN 256 + +static void +do_chmod(char *path, struct xs_permissions *perms, int nperms, int upto, + int recurse, struct xs_handle *xsh, xs_transaction_t xth) +{ + int ret; + + if (!path[0]) + return; + + ret = xs_set_permissions(xsh, xth, path, perms, nperms); + if (!ret) + err(1, "Error occurred setting permissions on ''%s''", path); + + if...

...ot;r-"; + case XS_PERM_READ|XS_PERM_WRITE: + return "rw"; + case XS_PERM_NONE: + return "--"; + default: + return "uu"; + } +} + +static void +do_stat(struct xs_handle *xsh, xs_transaction_t xth, char *path) +{ + unsigned int i, nperms; + struct xs_permissions *perms; + + perms = xs_get_permissions(xsh, xth, path, &nperms); + if (!perms) + errx(1, "Unable to get permission on %s\n", path); + + if (!nperms) { + free(perms); + errx(1, "Cannot determine owner of %s\n", path);...

[This email is either empty or too large to be displayed at this time]

Dear ecology fellows, I tried to implement Morisita-Horn distance (instead of Bray that is in the current version) in the code for the Simper analysis in vegan. I would be very grateful if someone can check if the code is right. function (comm, group, ...) { if (any(rowSums(comm, na.rm = TRUE) == 0)) warning("you have empty rows: results may be meaningless")

Could not find it in ?randomForest. Thank you for your help! -- Dimitri Liakhovitski Ninah.com Dimitri.Liakhovitski at ninah.com

Hi, I'm interested in incorporating some interaction terms between my explanatory variables (distance matrices in this case) when I'm using the function MRM of the package ecodist. The function is doing a multiple regression on distance matrices. I can get the function to work perfectly on my explanatory matrices by adding a (+) sign as known. However, when I try to use the (*) sign or

I would like to perform partial mantel tests on only within group values, with "between group" values assigned to NA. This is possible in package ncf partial.mantel.test, however this sues a different permutation to that used in ecodist.ecodist will not accept data with NA values, returning a "matrix is not square error. is it possible to perform this test in ecodist? many thanks

Dear all, Dose anyone know whether there is a good R packege or program for Mantel's randomization test? Thanks in advance. ------------------------ Takashi Mizuno zoono at sci.osaka-cu.ac.jp Plant Ecology Lab. Osaka City University ------------------------ -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

...;- x[dd,11:20] + u > y <- c(rep(1,10),rep(2,10)) > > data <- list ( x=x, y=y, geneid=as.character(1:nrow(x)), + genenames=paste("g",as.character(1:nrow(x))) ) > > samr.obj <- samr ( data, resp.type="Two class unpaired", + nperms=100, logged2=TRUE ) Error in samr(data, resp.type = "Two class unpaired", nperms = 100, logged2 = TRUE) : unused argument(s) (logged2 = TRUE) Error during wrapup: cannot open the connection > > > samr.obj <- samr ( data, resp.type="Two class unpaired", +...

Dear R Programmers, I am trying to run a Poisson regression on all pairs of variables in a data set and obtain the permutation distribution. The number of pairs is around 100000. It seems my code will take weeks to run, unless I try something else. Could you give me any suggestions on how to improve the speed of the code below, or any general suggestions on how I may accomplish this task. Thanks

Hi, I've got a dataset with 7 variables for 8 different species. I'd like to test the null hypothesis of no difference among species for these variables. MANOVA seems like the appropriate test, but since I'm unsure of how well the data fit the assumptions of equal variance/covariance and multivariate normality, I want to use a permutation test. I've been through CRAN looking at

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

Dear R-users, While making a prediction using the randomForest function (package randomForest) I'm getting the following error message: "Error in predict.randomForest(model, newdata = CV) : No forest component in the object" Here's my complete code. For reproducing this task, please find my 2 data sets attached ( http://www.nabble.com/file/p17855119/data.rar data.rar ).

...suggestions would be appreciated. thanks a lot. Mira ---------------------------------------------------------------------------------------------- #sub function for finding index rfind <- function(x)seq(along=x)[x*(1-x)>MAF*(1-MAF)] #sub function for permutation test perm.F = function(y,x,nperms,sites) { maxF = c(); for (i in 1:nperms) { F=numeric(S) #create an empty vector to store the F-values newY=sample(y,length(y)) #permute the cancer types newX = cbind(x, newY); # anova for all sites for ( i in sites ) { a <- anova(lm(newY~factor(newX[,i])...

Hi, I am trying to use a mantel test on two distance matrices. The code I have entered for each is: Gen_dists <- read.csv(file.choose(), stringsAsFactors = FALSE, na.strings = c(" "), fill = T,

Hi R-help, I am trying to use 'samr' for 10 pre and post paired samples to test whether post is different from pre (i.e., the location shift for the delta of (post-pre)). However, I got an error message saying > samr.obj<-samr(d, resp.type="Two class paired", nperms=100, random.seed=100) perm= 1 Error in !logged2 : invalid argument type Does anyone know what this means? or how to solve this error? Also, the options 'One Sample paired' is for testing the differences directly (d1, d2,...), I suppose. The source code is ****************************...

Hi! I am trying to analyse my data using amova (http://www.oga-lab.net/RGM2/func.php?rd_id=pegas:amova): My input to R is a DNA sequence file, format=fasta dna<- read.dna("XX.fasta", format="fasta") #left other options as default d<- dist.dna(dna, model="raw") g<- read.table("XXX.design") Load necessary libraries: library(pegas)

Dear R gurus I?m trying to use the GSCA package to a series of microarray data (prostate cancer normal vs tumor (29 vs 29 paired)) but I?m running into some problems. I have a matrix (named /data_final/) with 11k rows(genes) and 60 cols (58 samples (29N vs 29T), GO IDs, KEGG IDs). I also have a separate vector GS with the GO IDs mapped to genes (no duplicate genes but multiple IDs per gene like

Hi every body: I need some help with kendall.global. The example in the manual seems not working well, and cannot used with my data, always the same error. data(mite) > mite.hel <- decostand(mite, "hel") > > # Reproduce the results shown in Table 2 of Legendre (2005), a single group > mite.small <- mite.hel[c(4,9,14,22,31,34,45,53,61,69),c(13:15,23)] >