similar to: how to calculate a variance and covariance matrix for a vector

Hi. I have tried to use Package "SEM". As a learning, I try to convert a program running well of EQS which is as follows to SEM: ### EQS ### /SPECIFICATION CAS=100; VAR=5 MAT=COR; ANA=COR; /EQUATIONS V1=*F1+E1; V2=*F1+E2; V3=*F1+*F2+E3; V4=**F1+*F2*E4; V5=*F2+E5; /VAR E1 TO E5=*; F1*1.0; F2=1.0; /COV E1,E2=*; F1,F2=*: /PRINT FIT ALL; /MATRIX ...... /END This is the converted SEM

Anybody knows what functions can be used to calculate variance/covariance with complex numbers? var and cov don't seem to work: > a 1 V1 0.00810014+0.00169366i V2 0.00813054+0.00158251i V3 0.00805489+0.00163295i V4 0.00809141+0.00159533i V5 0.00813976+0.00161850i > var(a) 1 1 1.141556e-09 Warning message: In var(a) : imaginary parts discarded in

Hello, I am running a simulation on an old computer with memory of 512M. I constantly received a warning of "cannot allocate a vector of ...". How can I identify the matrix causing the problem? Is there any way to fix this issue? I would very appreciate your inputs. Thank you very much. Sincerely, Jialin Huang [[alternative HTML version deleted]]

Hello, I am very new to R (as my Subject probably indicates). I want to do something that should, I think, be very simple. I have five vectors in a list and I want to construct a covariance matrix out of them. Given a 5X5 matrix cvm1, and the list of vectors, cvm1_list, I thought the following would work (sorry cannot find code tags): for(i in 1:5){ for(j in 1:5){ cvm1[i,j] <-

I would appreciate confirmation that the function vcov(model.nlme) gives the var-cov matrix of the fixed effects in an nlme model. Presumably the random-effects var-cov matrix is given by cov(ranef (model.nlme)? Rob Forsyth

Dear all, Does this make any sense: var() = cov() != acf(lag.max=0, type="covariance")? I have daily data of IBM for May 2005, and I'm using the logarithmic return: > ibm200505$LRAdj.Close [1] NA 0.0203152 0.0005508 -0.0148397 -0.0025182 0.0092025 -0.0013889 [8] 0.0098196 -0.0103757 -0.0274917 0.0005716 -0.0159842 -0.0074306 0.0091710 [15] 0.0002898 0.0226306

Hello R list subscribers, I am trying to use the "by" command to create line-specific variance covariance matrices (where "x" is the original data matrix): by(x, x$line, function(d) { d.clean <- d[,-1]}) write.table(d.clean$line[1,1], sep = ",", file = "covariances.csv", col.names = FALSE, row.names = FALSE, append = TRUE) write.table("", sep

Hallo! I want to understand / recalculate what is done to get the CI of the logistic regression evaluated with lrm. As far as I came back, my problem is the variance-covariance matrix fit$var of the fit (fit<-lrm(...), fit$var). Here what I found and where I stucked: ----------------- library(Design) # data D<-c(rep("a", 20), rep("b", 20)) V<-0.25*(1:40) V[1]<-25

Dear all, I have a data frame with one factor and four numeric variables and wish to obtain the var-cor matrix separately by factor. I tried by() and sapply() but getting nowhere. I understand this can be done by subsetting the dataframe, but there should have some sleek ways of doing it. Here is a simulated dataframe; s <- rep(c("A","B","C"), c(25,22,18)) d

Hello, Sorry. It may be a stupid question. I have two vectors a<-c(9,3,5) b<-c(3,4,1) How can I get the variance-covariance matrix of these two vectors? I tried cov(a,b), I got a number not a matrix. I tried to transpose vector a and b as t(a) and t(b), it still cannot work. Any suggestions? Thank a lot! -- View this message in context:

Hi, I am trying to convert the following NLMIXED code to NLME, but am running into problems concerning 'Singularity in backsolve'. As I am new to R/S-Plus, I thought I may be missing something in the NLME code. NLMIXED *********** proc nlmixed data=kidney.kidney; parms delta=0.03 gamma=1.1 b1=-0.003 b2=-1.2 b3=0.09 b4=0.35 b5=-1.43 varu=0.5; eta=b1*age+b2*sex+b3*gn+b4*an+b5*pkn+u;

I would like to code the following in R: a1(b1+b2+b3) + a2(b1+b3+b4) + a3(b1+b2+b4) + a4(b1+b2+b3) or in summation notation: sum_{i=1, j\neq i}^{4} a_i * b_i I realise this is the same as: sum_{i=1, j=1}^{4} a_i * b_i - sum_{i=j} a_i * b_i would appreciate some help. Thank you. -- View this message in context: http://r.789695.n4.nabble.com/summation-coding-tp4646678.html Sent from the R

Hi, Excuse me for asking this silly question. But I really couldn't understand why cov() and ccov() don't work for my calculation of covariance matrix. a <- matrix(1:8, 2, 4) a [,1] [,2] [,3] [,4] [1,] 1 3 5 7 [2,] 2 4 6 8 > ccov(a) Error in solve.default(cov, ...) : Lapack routine dgesv: system is exactly singular I also tried colume bind, but it

Dear R users, Basically, from the following arbitrary data set: a <- data.frame(id=c(c("A1","A2","A3","A4","A5"),c("A3","A2","A3","A4","A5")),loc=c("B1","B2","B3","B4","B5"),clm=c(rep(("General"),6),rep("Life",4))) > a

Hi all, I have 4 matrices, each having 5 columns and 4 rows .....denoted by B1,B2,B3,B4. I have generated a vector of 7 indices, say (1,2,4,3,2,3,1} which refers to the index of the matrices to be chosen and then appended one on the top of the next: like, in this case, I wish to have the following mega matrix: B1over B2 over B4 over B3 over B2 over B3 over B1. 1> How can I achieve this?

Dear list, I have a list of matrices : i1 <- matrix(1:10, nrow = 2, ncol = 5) i2 <- matrix(11:20, nrow = 2, ncol = 5) j <- list(i1 = i1, i2 = i2) I would like to attribute names to each dimension, for each matrix, as follows : $i1 B1 B2 B3 B4 B5 A1 1 3 5 7 9 A2 2 4 6 8 10 $i2 B1 B2 B3 B4 B5 A1 11 13 15 17 19 A2 12 14 16 18 20 However, I have to use lapply function and attribute

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

Hi Matt, Run these commands on all the bricks of the replica pair to get the attrs set on the backend. On the bricks of first replica set: getfattr -d -e hex -m . <brick path>/.glusterfs/10/86/ 108694db-c039-4b7c-bd3d-ad6a15d811a2 On the fourth replica set: getfattr -d -e hex -m . <brick path>/.glusterfs/ e0/c5/e0c56bf7-8bfe-46ca-bde1-e46b92d33df3 Also run the "gluster volume

Hi all, I am struggling to work out how to use the rownames from an edited dataframe rather than the row names from the original dataframe. In my data set i'm trying to extract several rows of data on particular individuals, i don't doubt i'm using the long way round but what i have in the way of a script is this: ##selecting the IDs from the dataframe individually

I have a number of .ogg files encoded with b2, and some with b3. Is the psychoacoustic model in the forthcoming b4 significantly improved over these builds, such that re-encoding these files would give me either noticeably better sound, or smaller files? Also, just out of curiousity, will b4-encoded files be playable with older WinAmp plugins? Incidentally, I did some tests with b2 a while back