search for: byrow

...<- new.landscape.empty() penguinland <- new.intparam.land(penguinland,h=2,s=3) penguinland <- new.floatparam.land(penguinland,s=0) penguinland <- new.switchparam.land(penguinland,mp=0) ## local matrices are 3x3 (or greater than 2x2) matrixR <- matrix( c(rep(0.2, 3*3)), ncol=3,nrow=3,byrow=TRUE ) matrixS <- matrix( c(rep(0.1, 3*3)), ncol=3,nrow=3,byrow=TRUE ) matrixM <- matrix( c(1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0), ncol=3,nrow=3,byrow=TRUE ) penguinland <- new.local.demo(penguinland,matrixR,matrixS,matrixM) matrixRR <- matrix( c(rep(0,6*6)), ncol=6,nrow=6,byrow...

...I have added following code after the function is defined i.e. after #return(data.frame(macaulay_duration, modified_duration)) #} # Added code ONS  = read.csv('instrument details.csv') n      = length(ONS$par_value) par_value      =  matrix(data = ONS$par_value, nrow = n, ncol = 1, byrow = TRUE) coupon_rate    =  matrix(data = ONS$coupon_rate, nrow = n, ncol = 1, byrow = TRUE) freq_coupon  =  matrix(data = ONS$frequency_copoun, nrow = n, ncol = 1, byrow = TRUE) tenure          =  matrix(data = ONS$tenure, nrow = n, ncol = 1, byrow = TRUE) ytm             =   matrix(data = ONS$ytm,...

Suppose I have interest in a matrix with the following symbolic structure (specified by 3 parameters: sa, so, m): matrix(c(0,sa*m,so,sa),2,2,byrow=T) What I can't figure out is how to construct a series of matrices, where the elements/parameters are rnorm values. I'd like to construct separate matrices, with each matrix in the series using the 'next random parameter value'. While the following works (for generating, say, 5...

Hello: I am a novice R user, but I have been working my way through the manuals / tutorials, ... I have R / Deducer up and running, and know the basics. I want to analyze a microarray (gene expression) dataset. I need to input the data into R as a multidimensional (multi-way) array, something on the order of 15,000 x 3 x 8 x 2 [genes x replicates x time points x treatments] I've

Hello, I have initial parameters for HMM model and I want to use depmixS4 package. The parameters are in the form intial_prob_matrix=matrix(c(0.07614213, 0.45177665, 0.47208122), nrow=1, ncol=3, byrow = TRUE) transition_matrix=matrix(c(0.46666667,0.46666667,0.06666667, 0.06741573,0.5617978,0.37078652, 0.02173913,0.3478261,0.63043478), nrow = 3, ncol = 3, byrow = TRUE) meanval_matrix=matrix(c(545.1737,545.1737,803.5235, 565.7763,...

...representing the same information.) > > When I plot pie charts in a column, the piechart at the top of a column is > always smaller than the others: > > For example: > > layout( matrix(c(1,1,2, > 1,1,3, > 1,1,4), ncol=3, byrow=T)) > for(i in 1:4) piechart( 1:3 ) > > In column 3, the first piechart has a radius something like 2/3 of the > others. > > This also happens for: > > layout( matrix(c(1,2, > 1,3, > 1,4), ncol=2, byrow=T)) > for(i...

...and (p,n) respectively. I'd like to see if the is a fast way to compute a new matrix C with dimension (m*p,n) in which each row in C is found by applying some function f to each pair of rows (x,y) where x is a row in A and y is a row in B. For example, if A <- matrix(c(1, 2, 3, 4, 5, 6), byrow=TRUE, ncol=3) B <- matrix(c(7, 8, 9), byrow=TRUE, ncol=3) and f <- function(x,y) abs(y-x) then C <- matrix(c(6, 6, 6, 3, 3, 3), byrow=TRUE, ncol=3) Many thanks! Cheers, David

Dear all, I am trying to construct 25x31 different matrices of 2x2 dimension. Here is the problem: we have the following matrix matrix(c(54+s0, 43+s1, 56-s0, 67-s1), nrow=2, ncol=2, byrow=T) the values for s0 and s1 are c(0:24) and c(0:31), respectively. I wrote the following code without the desired results x<-0:24 y<-0:30 results<-array(matrix(0,nrow=2,ncol=2,byrow=T),dim=c(2,2,2...

...2-04-01","2012-05-01","2012-06-01", 0.56,0.45,0.21,0.64,0.36,0.08,152136,153081,155872,158356,162157,166226, 33.47,34.48,35.24,38.42,35.33,34.43,433,433,433,433,433,433,2005,2005,2005, 2005,2005,2005,3939,3939,3939,3939,3939,3939), nrow=18, ncol=3, byrow=FALSE, dimnames=list(c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18), c("date", "value", "code"))) dados433 <- matrix(data = NA, nrow = 6, ncol = 3, byrow= FALSE) dados2005 <- matrix(data = NA, nrow = 6, ncol = 3, byrow= FALSE) dados3939...

...re package than can do what we need already? I tried matrix with rep, but I am missing some magic there, since it doesn't do what we need. replicate might be promising, but then still need to rearrange the output into the column and row format we need. A simple example: mm=matrix(1:15,nrow=3,byrow = T) xmm=matrix(NA,nrow=nrow(mm)*3,ncol=ncol(mm)*3) for(icol in 1:ncol(mm)) { for(irow in 1:nrow(mm)) { xicol=(icol-1)*3 +c(1:3) xirow=(irow-1)*3 +c(1:3) xmm[xirow,xicol]=mm[irow,icol] } } mm > > mm > [,1] [,2] [,3] [,4] [,5] > [1,] 1 2 3 4 5 > [2,...

...at I've typed in and errors that I am getting. Thank you. Laura Table trying to enter: Diet: Binger-yes: Binger-No: Total: None 24 134 158 Healthy 9 52 61 Unhealthy 23 72 95 Dangerous 12 15 27 > diet=matrix(c(24,134,9,52,23,72,12,15),ncol=4,byrow=TRUE) > rownames(diet)=c("none", "healthy", "unhealthy", "dangerous") Error in dimnames(x) <- dn : length of 'dimnames' [1] not equal to array extent > diet=matrix(c(24,134,9,52,23,72,12,15), ncol=4, byrow=4) > rownanes(diet)=c("non...

Does anyone know how -log(x) can equal 743 but -log(x+0)=Inf? That's what the following stream of calculations suggest: Browse[2]> -log ( 1e-323+yMat2 - yMat1 * logitShape(matrix(parsList$Xs, nrow = numXs, ncol=numOfCurves), matrix(means, nrow = numXs, ncol=numOfCurves, byrow=TRUE), matrix(sigmas, nrow = numXs, ncol=numOfCurves, byrow=TRUE)) )[5,9] [1] Inf Yet: Browse[2]> logitShape(matrix(parsList$Xs, nrow = numXs, ncol=numOfCurves), matrix(means, nrow = numXs, ncol=numOfCurves, byrow=TRUE), matrix(sigmas, nrow = numXs, ncol=numOfCurves, byrow=TRUE))[5,9] [1] 1...

...double(n) for(i in 1:n){ y[i] <- sum(cumprod(rep(alpha, i)) * rev(x[1:i])) } y } loopRec(c(1, 2, 3), 0.5) ## This is a crazy solution, but worth giving it a try. charRec <- function(x, alpha){ n <- length(x) exp.mat <- matrix(rep(x, each = n), nc = n, byrow = TRUE) up.mat <- matrix(eval(parse(text = paste("c(", paste(paste(paste("rep(0, ", 0:(n - 1), ")", sep = ""), paste("cumprod(rep(", alpha, ",", n:1, "))"...

...rship. We then cbind these matrices with the trivial partitions into 1 and 6 groups: mat2g <- matrix(c(1,1,1,1,1, 2,1,1,1,1, 2,2,1,1,1, 2,2,2,1,1, 2,2,2,2,1, 2,2,2,2,2), nrow = 6, ncol = 5, byrow = TRUE) mat3g <- matrix(c(1,1,1,1,1,1,1,1,1,1, 2,2,2,2,1,1,1,1,1,1, 3,2,2,2,2,2,2,1,1,1, 3,3,2,2,3,2,2,2,2,1, 3,3,3,2,3,3,2,3,2,2, 3,3,3,3,3,3,3,3,3,3), nrow = 6, ncol = 10, byrow = TRUE)...

Dear ALL I have this function in R: func_LN <- function(data){ med_ge <- matrix(c(rep(NA,nrow(data)*ncol(data))), nrow = nrow(data), ncol=ncol(data), byrow=TRUE) T <- matrix(c(rep(NA,length(n)*ncol(data))), nrow = length(n), ncol=ncol(data), byrow=TRUE) Tdiff<- matrix(c(rep(NA,length(n)*ncol(data))), nrow = length(n), ncol=ncol(data), byrow=TRUE) T1<- c(rep(NA,ncol(data))) T0<- c(rep(NA,ncol(data))) cov_rank<-matrix(c(rep(NA,ncol(data)*...

Is there a way to get a copy of a matrix only for TRUE entries of a matching size boolean matrix? For *example*: > mat_letters <- matrix(data=c('A', 'B', 'C', 'D'), ncol=2, byrow=TRUE) > mat_letters [,1] [,2] [1,] "A" "B" [2,] "C" "D" > mat_bools <- matrix(data=c(FALSE, TRUE, TRUE, FALSE), ncol=2, byrow=TRUE) > mat_bools [,1] [,2] [1,] FALSE TRUE [2,] TRUE FALSE *Reminder:* The following is only an example...

...s but I kept receive the following massage: Error in A%*%x1 : non-conformable arguments. The massage appears even the -det() can be calculated and the dimensions are checked. here is my example although there might be no solution for the optimization problem. A=A myfn=function(A){ x=matrix(c(1.8,0),byrow=T) x1=matrix(c(.8,1.8),byrow=T) -det((t(x)-t(x1)%*%A)%*%(x-A%*%x1)) } A=matrix(c(1,.3,2,-1.2),byrow=T,nrow=2) optim(A,myfn) Another question regarding optimization: is there any chance that I can find a function or package that can do a constrained optimization such as: -det(x-A*x1-B*x2)' (x-A*...

Hi, how are you? I am trying to replicate the binary data f(2) function in the attached document by starting with the simple example found below: observed <- matrix(c(0, 1, 0, 0, 1, 1, 1, 0, 0),3,3,byrow=TRUE) data <- matrix(c(1, 1, 0, 0, 1, 0, 0, 0, 1),3,3,byrow=TRUE) f2 = sum(probability of the matrix element where the matrix element is present in both the observed and the data)/[sum(probability of the matrix element where the matrix element is present in both the observed and the data) + sum...

...ot) and the other one consisting of 4 image plots and a color table. I'd like the color table to be exactly the same. The way I proceded is the following: for one plot and the color-table png(file = png.file, width = 650, height = 800, pointsize = 16) layout(matrix(c(1, 2), ncol = 2, nrow = 1, byrow = T), widths = c(6, 1), heights = 1) par(mar = c(0.2, 0.2, 0.2, 0.2), mgp = c(2, 1, 0), las = 0) ... dev.off() for 4 plots and the color-table png(file = png.file, width = 650, height = 800, pointsize = 16) layout(matrix(c(1, 2, 5, 3, 4, 5), ncol = 3, nrow = 2, byrow = T), widths = c...

...c(1, "New England", 2, "Middle Atlantic", 3, "East North Central", 4, "West North Central", 5, "South Atlantic"), ncol=2, byrow=T) a$divisionNew[match(a$divisionOld, divisionTable[,1])] <- divisionTable[,2] But how do I handle the case where... a$divisionOld <- c(0,1,2,3,4,5) #no format available for 0, this throws an error. OR divisionTable <- matrix(c(1, "New England", 2, &...