It is a basic linear algebra issue and has nothing to do with statistics
I told you what your problem is and also how to solve it.
Ravi.
____________________________________________________________________
Ravi Varadhan, Ph.D.
Assistant Professor,
Division of Geriatric Medicine and Gerontology
School of Medicine
Johns Hopkins University
Ph. (410) 502-2619
email: rvarad...@jhmi.edu
----- Original Message -----
From: Moumita Das <das.moumita.onl...@gmail.com>
Date: Sunday, June 28, 2009 10:01 am
Subject: Re: [R] Error: system is computationally singular: reciprocal
condition number
To: r-help@r-project.org
> I get this error .... on using the, traceback() function :--
> > traceback()
> 10: .Call("La_dgesv", a, b, tol, PACKAGE = "base")
> 9: solve.default(Szz)
> 8: solve(Szz)
> 7: pcor.mat(firstvalue, secondvalue, third_var, method, na.rm = T)
> 6: PartialCorr_Calculation(value1, value2, third_var, method = "pearson",
> na.rm = T)
> 5: Partial(contrld_third_var(rowvalues$matrix1, rowvalues$matrix2,
> x <- stringOfItemCategoryToDataFrameOfItemCategory,
> item_category_table,
> pcor_type <- "ic"), data1, data2, pcor_thirdvar_type <- "all")
> 4: main()
> 3: eval.with.vis(expr, envir, enclos)
> 2: eval.with.vis(ei, envir)
> 1: source("correlationFP.R")
>
> what is this error ".*Call("La_dgesv", a, b, tol, PACKAGE = "base")*"
> how
> can i rectify it?
> I don't know statistics...going mad ,debugging this problem..
> any help is highly appreciated !! :)
>
> Thanks in advance
> Moumita
>
> On Thu, Jun 25, 2009 at 8:29 PM, Moumita Das
> <das.moumita.onl...@gmail.com>wrote:
>
> >
> > I get this error while computing partial correlation.
> >
> >
> > *Error in solve.default(Szz) :
> > system is computationally singular: reciprocal condition number =
> > 4.90109e-18*
> >
> > Why is it?Can anyone give me some idea ,how do i get rid it it?
> >
> > This is the function i use for calculating partial correlation.
> >
> >
> > pcor.mat <- function(x,y,z,method="p",na.rm=T){
> >
> >
> > x <- c(x)
> > y <- c(y)
> > z <- as.data.frame(z)
> >
> >
> >
> > if(dim(z)[2] == 0){
> > stop("There should be given data\n")
> > }
> >
> > data <- data.frame(x,y,z)
> >
> > if(na.rm == T){
> > data = na.omit(data)
> > }
> >
> > xdata <- na.omit(data.frame(data[,c(1,2)]))
> > Sxx <- cov(xdata,xdata,m=method)
> >
> > xzdata <- na.omit(data)
> > xdata <- data.frame(xzdata[,c(1,2)])
> > zdata <- data.frame(xzdata[,-c(1,2)])
> > Sxz <- cov(xdata,zdata,m=method)
> >
> > zdata <- na.omit(data.frame(data[,-c(1,2)]))
> > Szz <- cov(zdata,zdata,m=method)
> >
> >
> > # is Szz positive definite?
> > zz.ev <- eigen(Szz)$values
> > if(min(zz.ev)[1]<0){
> >
> > stop("\'Szz\' is not positive definite!\n")
> > }
> >
> > # partial correlation
> > Sxx.z <- Sxx - Sxz %*% solve(Szz) %*% t(Sxz)
> >
> > print(Sxx.z) # this gets printed
> >
> > rxx.z <- cov2cor(Sxx.z)[1,2] #some problem in this function
> > function (V)
> > {
> > print("cov2cor")
> > p <- (d <- dim(V))[1]
> > if (!is.numeric(V) || length(d) != 2L || p != d[2L])
> > stop("'V' is not a square numeric matrix")
> > Is <- sqrt(1/diag(V))
> > if (any(!is.finite(Is)))
> > warning("diag(.) had 0 or NA entries; non-finite result
> is
> > doubtful")
> > r <- V
> > r[] <- Is * V * rep(Is, each = p)
> > r[cbind(1L:p, 1L:p)] <- 1
> > r
> > }
> > return(rxx.z)
> > }
> >
> > --
> > Thanks
> > Moumita
> >
>
>
>
> --
> Thanks
> Moumita
>
> [[alternative HTML version deleted]]
>
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>
> PLEASE do read the posting guide
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