---------- Forwarded message ----------
From: thanoon younis <thanoon.younis80@gmail.com>
Date: Thursday, June 5, 2014
Subject: error in R program
To: Charles Determan Jr <deter088@umn.edu>


many thanks to you Dr. Charles
Really i have a problem with simulation data in xi  and now i have this
erro r   "Error in mvrnorm(1, c(0, 0, 0), phi1, tol = 1e-06, empirical =
FALSE,  :   incompatible arguments"
Regards



On 5 June 2014 16:45, Charles Determan Jr <deter088@umn.edu> wrote:

Hello again Thanoon,

Once again, you should send these request not to me but to the r-help
list.  You are far more likely to get help from the greater R community
than just me.  Furthermore, it is not entirely clear where your error is.
It is courteous to provide only the code that is run up to the error and
commenting the location.  I see you have a loop and all the more important
to isolate where the error is taking place and commenting it out.

My recommendations:

1. Set both t and i = 1 and try to run your loop code manually to locate
the specific function providing the error.
2. Check your data inputs for the function.  The error tells you that some
data is missing or infinite.  Perhaps a slight error in your data
generation?
3. See if you can address the specific instance before running the full
loops, it may be multiple instances or just one.

I hope this provides some guidance,

Charles


On Thu, Jun 5, 2014 at 3:10 AM, thanoon younis <thanoon.younis80@gmail.com>
wrote:

Dear Dr. Charles
i need your help to correct the winbugs code below to estimate parameters
of SEM by using bayesian inference for two group. I have this error"Error
in eigen(Sigma, symmetric = TRUE, EISPACK = EISPACK) : infinite or missing
values in 'x'.
many thanks in advance
R-CODE

library(MASS)      #Load the MASS package
library(R2WinBUGS) #Load the R2WinBUGS package
library(boa)       #Load the boa package
library(coda)      #Load the coda package
N1<-200;N2=100; P1<-10;P2<-10
phi1<-matrix(NA,nrow=4,ncol=4) #The covariance matrix of xi1
Ro1<-matrix(NA,nrow=4,ncol=4)
yo1<-matrix(data=NA,nrow=200,ncol=10)

phi2<-matrix(NA,nrow=4,ncol=4) #The covariance matrix of xi2
Ro2<-matrix(NA,nrow=4,ncol=4)
yo2<-matrix(data=NA,nrow=200,ncol=10)
#Matrices save the Bayesian Estimates and Standard Errors
Eu1<-matrix(data=NA,nrow=100,ncol=10)
SEu<-matrix(data=NA,nrow=100,ncol=10)
Elam1<-matrix(data=NA,nrow=100,ncol=6)
SElam1<-matrix(data=NA,nrow=100,ncol=6)
Egam1<-matrix(data=NA,nrow=100,ncol=4)
SEgam1<-matrix(data=NA,nrow=100,ncol=4)
Ephx1<-matrix(data=NA,nrow=100,ncol=4)
SEphx1<-matrix(data=NA,nrow=100,ncol=4)
Eb1<-numeric(100); SEb<-numeric(100)
Esgd1<-numeric(100); SEsgd<-numeric(100)
Eu2<-matrix(data=NA,nrow=100,ncol=10)
SEu2<-matrix(data=NA,nrow=100,ncol=10)
Elam2<-matrix(data=NA,nrow=100,ncol=6)
SElam2<-matrix(data=NA,nrow=100,ncol=6)
Egam2<-matrix(data=NA,nrow=100,ncol=3)
SEgam2<-matrix(data=NA,nrow=100,ncol=3)
Ephx2<-matrix(data=NA,nrow=100,ncol=3)
SEphx2<-matrix(data=NA,nrow=100,ncol=3)
Eb2<-numeric(100); SEb2<-numeric(100)
Esgd2<-numeric(100); SEsgd2<-numeric(100)

#Arrays save the HPD intervals
mu.y1=array(NA, c(100,10,2))
lam1=array(NA, c(100,6,2))
gam1=array(NA, c(100,4,2))
sgd1=array(NA, c(100,2))
phx1=array(NA, c(100,4,2))
mu.y2=array(NA, c(100,10,2))
lam2=array(NA, c(100,6,2))
gam2=array(NA, c(100,3,2))
sgd2=array(NA, c(100,2))
phx2=array(NA, c(100,3,2))
DIC=numeric(100)    #DIC values
#Parameters to be estimated
parameters<-c
("mu.y1","lam1","gam1","phi1","psi1","psd1","sgd1","phx1","mu.y2","lam2","gam2","phi2","psi2","psd2","sgd2","phx2")
#Initial values for the MCMC in WinBUGS
init1<-list(uby1=rep(0.0,10),lam1=rep(0.0,10),
gam1=c(1.0,1.0,1.0


-- 
Dr. Charles Determan, PhD
Integrated Biosciences

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