similar to: matrix not positive definite (while it should be)

Dear all, I know there have been various questions posted over the years about loops but I'm afraid that I'm still stuck. I am using Windows XP and R 2.9.2. I am generating some data using the multivariate normal distribution (within the 'mnormt' package). [The numerical values of sanad and covmat are not important.] > datamat <-

Hi All. I'd like to generate a sample of n observations from a k dimensional multivariate normal distribution with a random correlation matrix. My solution: The lower (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

What is 1. par.ests <- optimfit$par 2. fisher <- hessb(negloglik, par.ests, maxvalue=maxima); 3. varcov <- solve(fisher); 4. par.ses <- sqrt(diag(varcov)); Thanks a lot, fit.GEV <- function(maxima) { sigma0 <- sqrt((6. * var(maxima))/pi) mu0 <- mean(maxima) - 0.57722 * sigma0 xi0 <- 0.1 theta <- c(xi0, mu0, sigma0) #10/5/2007: removed assign() for maxima.nl

Dear list, I am trying to use the 'mvrnorm' function from the MASS package for simulating multivariate Gaussian data with given covariance matrix. The diagonal elements of my covariance matrix should be the same, i.e., all variables have the same marginal variance. Also all correlations between all pair of variables should be identical, but could be any value in [-1,1]. The problem I am

Please help me to produce smoothed contour plots. I have dependent data generated at regular intervals of two independent variables and would like to produce smoothed contour plots - I cannot get interp (alima) to produce cubic interpolations of the data, only linear ones. I'm interested in smoothing as the data generation process is stochastic and produces small variations which I'd

I used ggplot to create a scatter plot : library(ggplot) library(mnormt) Sigma = matrix(c(1, 0.6, 0.6, 1), 2, 2) x = rmnorm(20, c(0,0), Sigma) xx = x[order(x[,1]),] y = xx[,1] z = xx[,2] qplot(z, y, type="point", main="x-y plot", xlab="x", col="blue") However I want following: 1. Plot color must be Blue (where it is displaying as red) 2. There should not

> (FICB[,"temp"]) [1] "0.30" "0.55" "0.45" "2.30" "0.45" "0.30" "0.25" "0.30" "0.30" "1.05" "1.00" "1.00" [13] "0.30" "0.30" "0.30" "0.55" "0.30" "0.30" "0.30" "0.25" "1.00"

Hi, I have a question for my simulation problem: I would like to generate a positive (or semi def positive) covariance matrix, non singular, in wich the spectral decomposition returns me the same values for all dimensions but differs only in eigenvectors. Ex. sigma [,1] [,2] [1,] 5.05 4.95 [2,] 4.95 5.05 > eigen(sigma) $values [1] 10.0 0.1 $vectors [,1]

Dear list I'm testing a predictor and I produced nice performance plots with ROCR package utilizing the 3 standard command pred <- prediction(predictions, labels) perf <- performance(pred, measure = "tpr", x.measure = "fpr") plot(perf, col=rainbow(10)) The pred object and the perfo object are S4 with the following slots An object of class "performance"

(I use R 2.9.1 with win XP) If I run this code: seq(-0.1,.9, by = .05)[seq(-0.1,.9, by = .05) <= 0.5] I get this output: [1] -0.10 -0.05 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 Why is 0.50 not in the results ? (It seems that it gives a slightly bigger number then 0.5 but I don't understand why it does that) Where as if I try: seq(-0.1,.9, by = .05)[seq(-0.1,.9, by = .05) <=

To be fair I don't really understand what the error is telling me so I don't know how to correct it. I'm trying to reshape my data so that I do repeated measures ANOVA on it. The warning message: "Error in d[, timevar] <- times[1L] : subscript out of bounds" The program: library(QRMlib) library(Hmisc) ss<-5 mu<- c(0,2,3,12) mu2<- c(0,2,10,20)

Hi, I have a 3d array as below, I want to make this array to a matrix of p=50(rows) and n=20(columns) with the coverage values . The code before the array is: library(binom) Loading required package: lattice pi.seq<-seq(from = 0.01, to = 0.5, by = 0.01) no.seq<-seq(from = 5, to = 100, by = 5) cp.all = binom.coverage( p = pi.seq, n = no.seq , conf.level = 0.95, method = "exact")

Dear List: I am having some trouble reproducing some GLS estimates using matrix operations that I am not having with other R procedures. Here are some sample data to see what I am doing along with all code: mu<-c(100,150,200,250) Sigma<-matrix(c(400,80,16,3.2,80,400,80,16,16,80,400,80,3.2,16,80,400),n c=4) sample.size<-100 temp <-

Dear List: I am estimating a gls model and am having to make some rather unconventional modifications to handle a particular problem I have identified. My aim is to fit a GLS with an AR1 structure, obtain the variance-covariance matrix (V), modify it as needed given my research problem, and then reestimate the GLS by brute force using matrix operations. All seems to be working almost perfectly,

I have written a wrapper for heatmap.2 called heatmap.w.row.and.col.clust which auto-generates breaks using breaks<-round((c(seq(from=(-20 * stddev), to=(20 * stddev))))/20, digits = 2) #(stddev in this case = 2.5) This has always worked well in the past but now I am getting an error that non-finite breaks are being generated. Drilling down, it seems that my wrapper is generating finite

Hello there, I don't know if I'm addressing my question to the right e-mail address, I hope I do. Actually I have a little problem concerning writing a code in R. I try to briefly sum up my problem. As you can see below, I created the functions "Equation1" and "Equation2" with some conditions. Equation1 <-function(x){ if

Hi there, can anyone tell me how to extract to values of a particular slot for some S4 object? Let take following example: > library(fOptions) > val <-GBSOption(TypeFlag = "c", S = 60, X = 65, Time = 1/4, r = 0.08, b = 0.08, sigma = 0.30) > val Title: Black Scholes Option Valuation Call: GBSOption(TypeFlag = "c", S = 60, X = 65, Time = 1/4, r = 0.08, b =

Hello, I have a dataframe (test.df) with intervals that were generated by table (see below). I would prefer a dataframe labeled like this: > test.tab <- table(cut(skn.test$density, seq (0,1, by=0.05))) > test.tab (0,0.05] (0.05,0.1] (0.1,0.15] (0.15,0.2] (0.2,0.25] (0.25,0.3] (0.3,0.35] 68 87 85 72 65 73 74 (0.35,0.4] (0.4,0.45]

$ R --version R 1.6.1 (2002-11-01). So I would like to perform principal components analysis on a 16X16 correlation matrix, [princomp(cov.mat=x) where x is correlation matrix], the problem is princomp complains that it is not non-negative definite. I called eigen() on the correlation matrix and found that one of the eigenvectors is close to zero & negative (-0.001832311). Is there any way

Solaris 2.6, R Version 0.63.0 (November 14, 1998) Version 0.62.4 (October 24, 1998) > seq(0.15, 0.70, 0.05) [1] 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 The much reviled (on r-help) 0.62.3 got such simple cases right (even if 0.62.1 did not). Jim Lindsey insisted that such bugs are reported here, so I am. And yes, this is a real example and leads to an