search for: mat1

Hi, You could try: A<- matrix(unlist(read.table(text=" 1 2 3 4 5 6 7 8 9 9 8 7 6 5 4 3 2 1 ",sep="",header=FALSE)),ncol=3,byrow=FALSE,dimnames=NULL) library(matrixStats) ?res1<-t(sapply(split(as.data.frame(A),as.numeric(gl(nrow(A),2,6))),colProds)) ?res1 #? [,1] [,2] [,3] #1??? 4?? 10?? 18 #2?? 63?? 64?? 63 #3?? 18?? 10??? 4

HI, May be this helps: set.seed(24) ?mat1<- matrix(sample(1:60,30*24,replace=TRUE),ncol=24) colnames(mat1)<- rep(c("O","H","L","C"),6) indx<-seq_along(colnames(mat1)) n<- length(unique(colnames(mat1))) ?res<- lapply(split(indx,(indx-1)%%n+1),function(i) mat1[,i]) lapply(res,head,2) #$`...

Dear List, I want to compare the presence and absence of bird species based on the sites in a matrix. The matrix has 5 rows for Island A, B, C, D, and E. It has 100 columns for bird species D001-D100. In each cell of the matrix, the presence-absence of bird species will be recorded as 1 or 0. (For example, if species D001 is found on Island D, the matrix cell of species D001 and Island D

Hi, I was trying to make a 3D plot using densCols. The documentation for densCols doesn't look like it'll work for 3D. For example: ----------------------------------------- library(scatterplot3d) v1 <- rnorm(10000) v2 <- rnorm(10000) v3 <- rnorm(10000) ## 2D with denscols mat1 <- cbind(v1,v2) mcols1 <- densCols(mat1) plot(mat1,col=mcols1) mat <- cbind(v1,v2,v3) mcols <- densCols(mat) ## No go? ## 3D version with no densCols parameter scatterplot3d(mat,pch=16) ## gives error scatterplot3d(mat,col=mcols,pch=16) ----------------------------------------- Is...

Is there anyway to change any y[i] value (i=2,...6) to make following NLS workable? x <- c(0,5,10,15,20,25,30) y <- c(1.00000,0.82000,0.68000,0.64000,0.66667,0.68667,0.64000) lm(1/y ~~ x) nls(1/y ~~ a+b*x^c, start=list(a=1.16122,b=0.01565,c=1), trace=TRUE) #0.0920573 : 1.16122 0.01565 1.00000 #Error in numericDeriv(form[[3]], names(ind), env) : # Missing

....sm.odch", "toler.sm.odch") velik<-dim(result)[2] names(result)<-meno[1:velik] suma<-summary(fit) kateg<-trunc(smodch[1]/smodch[6]*1.41)+1 if (plotit) { par(mfrow=c(3,2)) posice<-volvzor*1:volop mat<-aggregate(vysledek,list(vzorek,operator),mean,na.rm=T) mat1<-aggregate(vysledek,list(vzorek,operator),sd,na.rm=T) #fig1 hmdm<-c(mean(mat$x)+(mean(mat1$x)/.9)/sqrt(opak)*3, mean(mat$x)-(mean(mat1$x)/.9)/sqrt(opak)*3) plot(mat$x,type="b",ylab="Prumery") abline(h=mean(mat$x),col=3) abline(h=h...

Dear all, I am using "coxme" function in Kinship library to fit random treatment effect nested within centre. I got 3 treatments (0,1,2) and 3 centres. I used following commands, but got an error. > ugroup=paste(rep(1:3,each=3),rep(0:2,3),sep='/') > mat1=bdsmatrix(rep(c(1,1,1,1,1,1,1,1,1),3),blocksize=rep(3,3),dimnames=list(ugroup,ugroup)) > mat2=bdsmatrix(rep(c(0,0,0,0,0,0,0,0,1),3),blocksize=rep(3,3),dimnames=list(ugroup,ugroup)) > group=paste(dat1$centre,dat1$treat,sep='/') > coxme(Surv(time,status) ~ as.factor(treat), data=...

I have a matrix of 5 columns and 64 rows, let's call it "mat1". All values are 1 or 0. I need to take the values of the elements by row and create a single numeric element that can be placed its respective slot in a 64-element list, named "list1". For example, mat1[11,1:5] = 0,1,1,0,1 and I must put 01101, with a length of 1, into the 11th e...

...ckage coxme in order to build Cox models with complex random effects. Unfortunately, I sometimes get surprising estimations of the variances of the random effects. I ran models with different fixed covariates but always with the same 3 random effects defined by the argument varlist=coxmeMlist(list(mat1,mat2,mat3), rescale = F, pdcheck = F, positive=F). I get a few times exactly the same estimations of the parameters of the random effects whereas the fixed effects of the models are different: Random effects Group Variable Std Dev Variance idp Vmat.1 0.10000000 0.01000000 Vmat.2...

R-3.0.1 rev 62743, binary downloaded from CRAN just now; macosx 10.8.3 Hello, eigen(symmetric=TRUE) behaves strangely when given complex matrices. The following two lines define 'A', a 100x100 (real) symmetric matrix which theoretical considerations [Bochner's theorem] show to be positive definite: jj <- matrix(0,100,100) A <- exp(-0.1*(row(jj)-col(jj))^2) A's being

...the real data does return information about the coefficients and the model fitting. I'm grateful for any help. I'm aware that the topic of non-integer successes has been addressed before, but I could not find my answer to this question. Yours, Simon Kiss ######str() on original data str(mat1) 'data.frame': 1001 obs. of 5 variables: $ prov : Factor w/ 4 levels "Ontario","PQ",..: 2 2 2 2 2 2 2 2 2 2 ... $ edu : Factor w/ 2 levels "secondary","post-secondary": 2 2 2 1 1 2 2 2 1 1 ... $ gender: Factor w/ 2 levels "Male...

...Program to read and create Matrix matrixmy<-scan("test.csv",sep=",",skip=1) arr=array(0,dim=c(4,4)) a<-1 while(a<=length(matrixmy)) { i=matrixmy[a] a<-a+1 j=matrixmy[a] a<-a+1 k=matrixmy[a] arr[i,j]<-k arr[j,i]<-k a<-a+1 } Created matrix mat1<-matrix(data=arr,nrow=4,ncol=4) g<-graph.adjacency(adjmatrix=mat1,mode=c("undirected")) betweenness(g) The answer I get is 0 0 0 0 I was expecting Node 1 will have high value. Can someone tell me why it is happening like this. Thanks in advance. Nathna -- View this mess...

Hello together, this is ma first post, so please aplogize me if post this in the wrong section. I have problem concerning ma two matrizes. After a regressione and so on, I got two matrizes Matrixres contains the results of ma calculation. Matrixr contains my detiene, which where Aldo used for the regression. Please ser the following code: #Datei einlesen residual =

...multiplications, I have a situation in in which the result depends on the nature of nearby I/O. Thus, with all arithmetic done with type double, and where values are mostly in the range [-1.0e0,+1.0e0] or nearby, I do: cerr << "some stuff" << endl; mat3 = matmult(mat1,mat2); I get a difference of the order 1.0e-15 depending on whether the cerr line does or does not end in "endl" as shown. I am imagining that there is some "randomness" in the roundoff that depends on the I/O situation. Is this credible? Any other suggestions? Thanks f...

Hi, Not sure if this is what you wanted: ?mat1<- as.matrix(read.table(text=" ????? 33??????? 45??? 50 ????? NA?????? NA?? 54 ",sep="",header=FALSE)) mat2<- as.matrix(read.table(text=" 24??????????????? 0.0000000??????? 0.0000000 0.0000000??????? 14??????????????? 0.0000000 0.0000000 0.0000000??????? 10 ",sep=...

Dear useRs, i am using NbClust to determine appropriate number of cluster for hclustering. i am consistently getting the following error Error in if (is.na(n) || n > 65536L) stop("size cannot be NA nor exceed 65536") : missing value where TRUE/FALSE needed Does any one know where i am wrong?? thanks in advance eliza [[alternative HTML version deleted]]

[[1]]              [,1]      [,2] [1,]  0.003632559 0.2190629 [2,] -2.090772847 0.2190629 [[2]]             [,1]       [,2] [1,] 0.004278991 0.04337005 [2,] 0.190723602 0.04337005 [[3]]            [,1]        [,2] [1,] 0.01237827 -0.01544811 [2,] 0.06452200 -0.01544811 g<-c(1,2,3) function(g,hessianList){    for(i in 1:3){          ft1<-g[2]*hessianList[[2]]          ft2<-sum(ft1)   

Dear list, I used read.xls to read in an excel file, which has both character and numeric columns. Everything seems fine except in the last column, I have this character value "ng/ml". When reading in, read.xls seems to add a space at the end of it, became "ng/ml ". How can I prevent read.xls doing so? Thanks. >read.xls(data, header=T, as.is=T) Jun [[alternative

Dear All, My question is: how can I estimate VECM system with "unrestricted trend" (aka "case 5") option as a deterministic term? As far as I know, ca.jo in urca package allows for "restricted trend" only [vecm <- ca.jo(data, type = "trace"/"eigen", ecdet = "trend", K = n, spec = "transitory"/"longrun")].

Dear all, I am trying to split a matrix into 2 as efficiently as possible. It is a character matrix: 1 2 3 1 "2-271" "2-367" "1-79" 2 "2-282" "2-378" "1-90" 3 "2-281" "2-377" "1-89" I want to make 2 matrices from this, as succinctly and efficiently as possible. I've tried such