search for: nperm

..., 3, 7, -2, -6, -6, -5, -2, -1) The Monte Carlo permutation approach seems to get me part way. # calculate the number of runs in the data vector nruns <- function(x) { signs <- sign(x) runs <- rle(signs) r <- length(runs$lengths) return(r) } MC.runs <- function(x, nperm) { RUNS <- numeric(nperm) for (i in 1:nperm) { RUNS[i] <- nruns(sample(x)) } cdf <- cumsum(table(RUNS))/nperm return(list(RUNS=RUNS, cdf=cdf, nperm=nperm)) } MC.runs(dtemp, 100000) Thanks. --Dale

...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);...

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...ength(permutations) == 1) { perm <- shuffleSet(nobs, permutations, ...) } else { perm <- permutations } if (ncol(perm) != nobs) stop(gettextf("'permutations' have %d columns, but data have %d rows", ncol(perm), nobs)) nperm <- nrow(perm) if (nperm > 0) perm.contr <- matrix(nrow = P, ncol = nperm) for (i in 1:nrow(comp)) { group.a <- comm[group == comp[i, 1], ] group.b <- comm[group == comp[i, 2], ] n.a <- nrow(group.a) n.b <- nrow(group.b) c...

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

...ng a (+) sign as known. However, when I try to use the (*) sign or (:) between two terms that I'd like to check the interaction for, I get same results as with just additive model. Is there something that I do wrong? Thanks, *For example: * > mrm.S10z_A<- MRM(Adis ~ S10z.st+Distance.st, nperm=100) > mrm.S10z_A $coef Adis pval Int 0.553605282 0.06 S10z.st -0.002587542 0.86 Distance.st 0.028866573 0.07 $r.squared R2 pval 0.004180399 0.060000000 $F.test F F.pval 4.634535 0.060000 > mrm.S10z_A<- MRM(Adis ~ S10z.st*Di...

...(ncf) > x<-sample(1:1000,20) > y<-sample(1:1000,20) > z<-sample(1:1000,20) > M1<-as.matrix( distance(x)) > M2 <-as.matrix( distance(y )) > M3<-as.matrix( distance(z )) > D1<-(lower(M1)) > D2<-(lower(M2)) > D3<-(lower(M3)) > mantel(D1 ~ D2+D3, nperm=1000) mantelr pval1 pval2 pval3 llim.2.5% ulim.97.5% 0.09014696 0.10300000 0.89800000 0.18400000 0.01857311 0.18468621 > partial.mantel.test(M1,M2,M3,quiet=T) $MantelR r12 r13 r23 r12.3 r13.2 0.08977575 0.02170997 -0.01561346 0.0901469...

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", +...

...e data; the actual data is 700x800 myData <- matrix(sample(c(1:3),500,replace=TRUE),nrow=100,ncol=5) # the response is binary response <- c(rep(1,50),rep(0,50)) # initalize permutation of response 'labels'. perm.response <- response counts <- rep(1,18) # Number of permutations nperm <- 5 # matrix of all pairs of indices all.pairs <- combn2(1:ncol(myData)) # initalize results pmatrix <- matrix(-1,nrow=nperm,ncol=nrow(all.pairs)) getLRTpval <- function(index) { # A contingency table is formed from two columns of the data and the response (3 way table) and made i...

...t <- c() for (i in 1:1000){ wilks <- summary.manova(manova(maxent~sample(max.spec)), test="Wilks")$stats[1,2] result.vect <- c(res.vect,wilks) } maxent is the data, max.spec is a vector of species names. Comparing the result.vect with the wilks value for the unpermuted data suggests there are very significant differences among species -- but did I do this properly? -- Regards, Tyler Smith

....getdropbox.com/u/1047321/syrjala_data_cod.csv > syrjala_data_cod.csv") dataCod = read.csv(file = "syrjala_data_cod.csv", header = TRUE) # Normalize abundances dataCod$var1 = dataCod$var1/sum(dataCod$var1) dataCod$var2 = dataCod$var2/sum(dataCod$var2) # Number of permutations nperm = 1000 # Create nperm-1 replicates of the data (one is the original observation) d = rep(list(dataCod), nperm-1) # Compute number of observations before to avoid doing that for every replicate n = nrow(dataCod) require(plyr) # Permute some observations and compute the syrjala stat for each...

....txt",header=TRUE,sep="\t") # Both CT & CV have the syntaxis X1, X2,...,X97,Y where all variables are numeric x <- CT[,-98] y <- CT[,98] xtest <- CV[,-98] ytest <- CV[,98] library(randomForest) model <- randomForest(x ,y , xtest, ytest,ntree=500,mtry=32,nodesize=5,nPerm=2) model #Call: # randomForest(x = x, y = y, xtest = xtest, ytest = ytest, ntree = 500, mtry = 32, nodesize = 5, # nPerm = 2) # Type of random forest: regression # Number of trees: 500 #No. of variables tried at each split: 32 # # Mean of squared re...

...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]...

...na.strings = c(" "), fill = T, header = FALSE, blank.lines.skip = FALSE) Gen_matrix <- as.matrix(Gen_dists) mantel.test(Geo_matrix, Gen_matrix, nperm = 999, graph = T, alternative = "great") I am getting a result but with lots of warning messages : "Warning: non-square matrix" However, my matrices are square! Has anyone else had this problem? Many thanks. [[alternative HTML version deleted]]

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 ***************************...

...eft other options as default d<- dist.dna(dna, model="raw") g<- read.table("XXX.design") Load necessary libraries: library(pegas) Loading required package: adegenet Loading required package: MASS Loading required package: ade4 Running Amova: amova(d ~ g, nperm = 100) Error in FUN(X[[1L]], ...) : 'bin' must be numeric or a factor How can I solve this "bin" problem? I think it might be a problem with the g variabel. In the example they type g <- factor(c(rep("A", 7), rep("B", 8))) I cannot find any informa...

...inal/) 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 in col 59) However when I try to run: /singleDC(data_final, group=c(1:29,30,58), GSdefList = GS, nperm = 3, permDI=TRUE)/ I get an error/ ?unused argument(s) (permDI=TRUE)?/ When I run it without that argument it runs indefinitely even if I use only 100 rows data_final[1:100,]. Furthermore when I block the computation I get the error: /Error in `[.data.frame`(fixed.gs.data, , (csum.group[i-1] +...

...ups > group <-c(1,1,2,1,1,1,1,1,2,1,1,1,1,1,1,2,1,2,1,1,1,1,2,1,2,1,1,1,1,1,2,2,2,2,2) > kendall.global(mite.hel, group=group) Errore in FUN(newX[, i], ...) : .Random.seed is not an integer vector but of type 'list' > kendall.post(mite.hel, group=group, mult="holm", nperm=99) Errore in sample(R.gr[, j]) : .Random.seed is not an integer vector but of type 'list' Thank you very much if you know how to solve it. Rosa. [[alternative HTML version deleted]]