search for: cormat

...ower (or upper) triangle of the correlation matrix has n.tri=(d/2)(d+1)-d entries. Take a uniform sample of n.tri possible correlations (runi(n.tr,-.99,.99) Populate a triangle of the matrix with the sampled correlations Mirror the triangle to populate the other triangle forming a symmetric matrix, cormat Sample n observations from a multivariate normal distribution with mean vector=0 and varcov=cormat Problem: This approach violates the triangle inequality property of correlation matrices. ?So, the matrix I've constructed is certainly a valid matrix but it is not a valid correlation matrix an...

.../(sqrt(mb*mc)) # Grad wrt a @ g[2]<-(-0.5)*(cor/mb) # Grad wrt b @ g[3]<-(-0.5)*(cor/mc) # Grad wrt c @ vcor<-t(g)%*%vd%*%g secor<-sqrt(vcor) list(cor=cor,secor=secor) } acf2<-function(x,y=x,lagmax=(length(x)-10),m="ACF",mix=0){ L<-min(length(x),length(y)) cormat<-matrix(0,lagmax,1) secormat<-matrix(0,lagmax,1) x<-x[1:L] y<-y[1:L] c0<-initc(x,y) cx0<-c0$cx0 cy0<-c0$cy0 for (i in 1:lagmax){ x1<-x[1:(L-i)] y1<-y[(i+1):L] c<-corse(x1,y1,cx0,cy0) cormat[i]<-c$cor secormat[i]<-c$secor } stop...

...pData <- data.frame(id = rep(paste("JohnDoe", 1:n, sep = "_"), each = t),time = rep(1981:1984, n)) rho <-0.95 alphai <- rnorm(n,mean=0,sd=1)#alphai simulation x<- as.matrix(rnorm(nt,1))#xi simulation akro <- kronecker(alphai ,matrix(1,t,1))#kronecker of alphai cormat<-matrix(c(1,rho,rho,1),nrow=2,ncol=2)#correlation matrix cormat.chold <- chol(cormat)#choleski transformation of correlation matrix akrox <- cbind(akro,x) ax <- akrox%*%cormat.chold ai <- as.matrix(ax[,1]) pData$alphai<-as.vector(ai) xcorr <- as.matrix(ax[,2:(1+ncol(x))]) pData...

...pData <- data.frame(id = rep(paste("JohnDoe", 1:n, sep = "_"), each = t),time = rep(1981:1984, n)) rho <-0.95 alphai <- rnorm(n,mean=0,sd=1)#alphai simulation x<- as.matrix(rnorm(nt,1))#xi simulation akro <- kronecker(alphai ,matrix(1,t,1))#kronecker of alphai cormat<-matrix(c(1,rho,rho,1),nrow=2,ncol=2)#correlation matrix cormat.chold <- chol(cormat)#choleski transformation of correlation matrix akrox <- cbind(akro,x) ax <- akrox%*%cormat.chold ai <- as.matrix(ax[,1]) pData$alphai<-as.vector(ai) xcorr <- as.matrix(ax[,2:(1+ncol(x))]) pData...

...*t pData <- data.frame(id = rep(paste("JohnDoe", 1:n, sep = "_"), each = t),time = rep(1981:1984, n)) rho <-0.95 alphai <- rnorm(n,mean=0,sd=1)#alphai simulation x<- as.matrix(rnorm(nt,1))#xi simulation akro <- kronecker(alphai ,matrix(1,t,1))#kronecker of alphai cormat<-matrix(c(1,rho,rho,1),nrow=2,ncol=2)#correlation matrix cormat.chold <- chol(cormat)#choleski transformation of correlation matrix akrox <- cbind(akro,x) ax <- akrox%*%cormat.chold ai <- as.matrix(ax[,1]) pData$alphai<-as.vector(ai) xcorr <- as.matrix(ax[,2:(1+ncol(x))]) pData...

...frame(id = rep(paste("JohnDoe", 1:n, sep = "_"), each = t),time = rep(1981:1984, n)) rho <-0.95 alphai <- rnorm(n,mean=0,sd=1)#alphai simulation x<- as.matrix(rnorm(nt,1))#xi simulation akro <- kronecker(alphai ,matrix(1,t,1))#kronecker of alphai cormat<-matrix(c(1,rho,rho,1),nrow=2,ncol=2)#correlation matrix cormat.chold <- chol(cormat)#choleski transformation of correlation matrix akrox <- cbind(akro,x) ax <- akrox...

Hi I'm trying to write a generic script for processing some data which finishes off with some plots. Given Im never sure how many columns will be in my dataframe I wanted to using the following plot(spectra.wavelength, cormat, type = "l", ylim=c(-1,1), xlab="Wavelength (nm)", ylab="Correlation") however even if I specify as type="l" it appears plot as points (right hand plot). If I specify a range such as plot(650:700, cormat, type = "l", ylim=c(-1,1), xlab="Wave...

...rks very nicely as shown here: library(Hmisc) library(corpcor) # Create example data x1 = runif(50) x2 = runif(50) x3 = runif(50) d = data.frame(x1=x1,x2=x2,x3=x3) label(d$x1) = "Variable x1" label(d$x2) = "Variable x2" label(d$x3) = "Variable x3" # Get correlations cormat = cor(d) # Get vector form of lower triangular elements cors = sm2vec(cormat,diag=F) inds = sm.index(cormat,diag=F) # Create a data frame var1 = dimnames(cormat)[[1]][inds[,1]] var2 = dimnames(cormat)[[2]][inds[,2]] lbl1 = label(d[,var1]) lbl2 = label(d[,var2]) cor_df = data.frame(Var1=lbl1,Var2=...

...ot() line col.l <- colorRampPalette(c('blue', 'green', 'yellow', 'red'))(30) for (i in 1:10) { randmat <- cbind(rnorm(100),rnorm(100),rnorm(100),rnorm(100),rnorm(100)) colnames(randmat) <- c('A','B','C','D','E') cormat <- cor(randmat) levelplot(cormat,xlab=NULL,ylab=NULL,main='Correlation Heat Map',col.regions=col.l) # heatmap(cormat,col=col.l,Rowv=NA, Colv=NA,main=paste("Plot:",i,sep='')) Sys.sleep(1) } Thanks much in advance! [[alternative HTML version deleted]]

Hi, I was trying to evaluate the pearson correlation and the p-values for an nxm matrix, where each row represents a vector. One way to do it would be to iterate through each row, and find its correlation value( and the p-value) with respect to the other rows. Is there some function by which I can use the matrix as input? Ideally, the output would be an nxn matrix, containing the p-values

Dear R community: I want to simulate a regression matrix which is generated from an orthonormal matrix X of dimension 30*10 with different between-column pairwise correlation coefficients generated from uniform distribution U(-1,1). Thanks in advance! Rui [[alternative HTML version deleted]]

Dear R users, Simple question. Can anyone help with the code that would allow me to view only the variables who's correlation output is >0.8? This is the code I'm using to date >cor(data, method="spearman") Kind regards Krys -------------------------------------------- _________________________________________________________________ Save time by

...nDoe", 1:n, sep = "."), each = t),time = rep(1981:1985, n)) rho <-0.99#simulate alphai corelated with the xi print(rho) alphai <- rnorm(n,mean=0,sd=1)#alphai simulation x<- as.matrix(rnorm(nt,1))#xi simulation akro <- kronecker(alphai ,matrix(1,t,1))#kronecker of alphai cormat<-matrix(c(1,rho,rho,1),nrow=2,ncol=2)#correlation matrix cormat.chold <- chol(cormat)#choleski transformation of correlation matrix akrox <- cbind(akro,x) ax <- akrox%*%cormat.chold ai <- as.matrix(ax[,1]) pData$alphai<-as.vector(ai) xcorr <- as.matrix(ax[,2:(1+ncol(x))]) pData...

...The code is ################# r2test <- function(df, cutoff=0.8) { if (cutoff > 1 || cutoff <= 0) { stop(" 0 <= cutoff < 1") } if (!is.matrix(d) && !is.data.frame(d)) { stop("Must supply a data.frame or matrix") } r2cut = sqrt(cutoff); cormat <- cor(d); bad.idx <- which(abs(cormat)>r2cut,arr.ind=T); bad.idx <- matrix( bad.idx[bad.idx[,1] > bad.idx[,2]], ncol=2); drop.idx <- ifelse(runif(nrow(bad.idx)) > .5, bad.idx[,1], bad.idx [,2]); if (length(drop.idx) == 0) { 1:ncol(d) } else { (1:ncol(d))[...

...gt; library (mvtnorm) > > options(digits=2) > > # Set variable name # > namelist1 <- c("Age", "Gender", "SES", "ISS", "IQTe", "PreTe") > > # Generate Data # > Sample.Size <- 10 # Variable Sample Size # > CorMat <- matrix(c(1.0 ,0.0 ,0.0 , 0.5, 0.2, 0.5, > 0.0, 1.0 ,0.0, 0.0, 0.0, 0.0, > 0.0 ,0.0 ,1.0, 0.5, 0.4, 0.5, > 0.5, 0.0, 0.5, 1.0, 0.4, 0.7, > 0.2, 0.0, 0.4, 0.4, 1.0, 0.9, > 0.5, 0.0, 0.5, 0.7, 0.9, 1.0),ncol=6) # Correlation Matrix # > > DataSet &lt...

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Newsgroup members, Does anyone have a clever way to simulate a correlation matrix such that each column contains dichotomous variables (0,1) and where each column has different prevalence rates. For instance, I would like to simulate the following correlation matrix: > CORMAT[1:4,1:4] PUREPT PTCUT2 PHQCUT2T ALCCUTT2 PUREPT 1.0000000 0.5141552 0.1913139 0.1917923 PTCUT2 0.5141552 1.0000000 0.2913552 0.2204097 PHQCUT2T 0.1913139 0.2913552 1.0000000 0.1803987 ALCCUTT2 0.1917923 0.2204097 0.1803987 1.0000000 Where the prevalence for each variable is:...

...I read also that it is possible to use a point-biserial correlation to calculate the correlation between dichotomous and continuous variables. Now I am confused what I should use to calculate the correlation using all my variables and how I could do that in R. Is it possible with cor(), rcorr(), cormat() or other R-functions using one of the available correlation-coefficients. I would be very happy if somebody could enlighten my darkness. Many thanks in advance. B. ----- The art of living is more like wrestling than dancing. (Marcus Aurelius) -- View this message in context: http://www.nabb...

Dear listers, I was used to package pgirmess under Windows with everything OK, but, for the first time, I had a trial this afternoon on Ubuntu 7.10 gutsy (I have a double boot computer and work more and more under unix) and R 2.7.0. Everything went OK except this: sudo R CMD check pgirmess ..... * checking examples ... ERROR Running examples in 'pgirmess-Ex.R' failed. The error most

...se I can source them all without error). At this point, I am completelly lost. Any help would be welcome. This package contains several hundreds of lines of code, and NAMESPACE is quite complex: importFrom(svMisc, listCustoms, getTemp) importFrom(R2HTML, HTML, HTMLhr, HTMLInsertGraph, HTMLli, HTML.cormat) importFrom(utils, browseURL, methods) importFrom(lattice, lset) importFrom(MASS, lda) import(svIO, graphics, grDevices, stats) export(guiViewsCmd, guiViewsCSS, guiViewsCSSChange, guiViewsDir, guiViewsDisplay, guiViewsFile, report, reportGraph,...