[R-meta] Non-positive definite variance-covariance matrix

[E. van der Meulen]

Dear James and the rest,

Thanks for your help. I hope I put the adress in my reply. Your q really helped, unfortunately I still have some unresolved matrices for which I probably will need to find a different solution.

Kind regards,

Erik
________________________________
Van: James Pustejovsky [jepusto using gmail.com]
Verzonden: dinsdag 27 augustus 2019 17:01
Aan: E. van der Meulen; R meta
Onderwerp: Re: [R-meta] Non-positive definite variance-covariance matrix

Erik,

Please keep the listserv cc'd in your replies.

How is the example that you sent formatted? If I read it in as a matrix, then it does not appear to be a feasible covariance matrix because the off-diagonal entries are all larger than the diagonals. Example below.

James

V <- matrix(c(.00825680, .00994727,
              .00959348, .01321687,
              .01290479, .01133872,
              .00994727, .00825568,
              .00937481, .01966662,
              .01270540, .01118369,
              .00959348, .00937481,
              .00824400, .01269867,
              .01964920, .01392237,
              .01321687, .01966662,
              .01269867, .00826429,
              .00938956, .03499702,
              .01290479, .01270540,
              .01964920, .00938956,
              .00826134, .01393701,
              .01133872, .01118369,
              .01392237, .03499702,
              .01393701, .00818451), 6, 6)

cov2cor(V)


On Sat, Aug 24, 2019 at 3:26 PM E. van der Meulen <E.vdrMeulen_1 using uvt.nl<mailto:E.vdrMeulen_1 using uvt.nl>> wrote:
Dear James (and others),

Thank you for your assistance. Especially using the matrixcalc package, it resolved the problem so far that the problem appears to be related to the proces I used to formulate by matrices, and probably not content wise. This because all matrices with 6 dimensions were off, and none of the others. However, when I check these matrices I simply can't see what is wrong with them. The data is correctly sorted by ID. I tried using sapply, but this did not change much. Under here is an example matrix with 6 dimensions, does anybody else see why it is not positive definite?


.00825680 .00994727 .00959348 .01321687 .01290479 .01133872
.00994727 .00825568 .00937481 .01966662 .01270540 .01118369
.00959348 .00937481 .00824400 .01269867 .01964920 .01392237
.01321687 .01966662 .01269867 .00826429 .00938956 .03499702
.01290479 .01270540 .01964920 .00938956 .00826134 .01393701
.01133872 .01118369 .01392237 .03499702 .01393701 .00818451

Thanks again!

Kind regards,

Erik
________________________________
Van: James Pustejovsky [jepusto using gmail.com<mailto:jepusto using gmail.com>]
Verzonden: maandag 19 augustus 2019 3:39
Aan: E. van der Meulen
CC: r-sig-meta-analysis using r-project.org<mailto:r-sig-meta-analysis using r-project.org>
Onderwerp: Re: [R-meta] Non-positive definite variance-covariance matrix

Erik,

A few possibilities occur to me:
1) Is the data frame sorted by ID? If not, then the split-calculate-bldiag calculations will return a matrix that is not in the same order as the original data frame.
2) Why do you use sapply rather than lapply in creating the remove_zero_mat object? Perhaps it makes no difference.
3) To further isolate the problem, it might be useful to check for positive-definiteness of the component covariance matrices. The matrixcalc package provides a handy function for doing so:
library(matrixcalc)
lapply(remove_zero_mat, is.positive.definite)

James

On Sun, Aug 18, 2019 at 12:42 PM E. van der Meulen <E.vdrMeulen_1 using uvt.nl<mailto:E.vdrMeulen_1 using uvt.nl>> wrote:
Dear all,

I am trying to run a multivariate meta-analysis for a review. For this review I included multiple effect sizes from single studies into my analysis. The number of effect sizes from a single study range from 1 to 36. To account for covariance between effect sizes extracted from the same sample, I created a variance-covariance matrix for each study with multiple effect sizes (which is the majority). I am using a syntax I have used before, in the previous attempt it worked perfectly. However, in this new study I am continuously ending up with the same error message:

Error in .ll.rma.mv<http://ll.rma.mv>(opt.res$par, reml = reml, Y = Y, M = V, A = A, X.fit = X,  :
  Final variance-covariance matrix not positive definite.
In addition: Warning message:
In rma.mv<http://rma.mv>(dat$ESP, V, mods = ~1, random = list(~1 | id/nummer),  :
  'V' appears to be not positive definite.

In which V is the variance-covariance matrix I made. As far as I know an error due to 'non-positive definite matrices' can occur in cases in which negative or exactly zero eigenvalues appear anywhere in any of the matrices. As far as I can determine this is not the case. What could be the problem? If it helps this is full the syntax:


library(metafor) # For meta-analysis
library(clubSandwich) # For cluster-robust variance-covariance matrix
library(foreign)

dat<- read.spss("TEST.sav", to.data.frame= TRUE)

list_mat<- split(dat[ ,c("v1p", "v2p", "v3p", "v4p", "v5p", "v6p")], dat$id)

remove_zero<- lapply(list_mat, function(x) x[ ,colSums(x) != 0])

remove_zero_mat<- sapply(remove_zero, as.matrix)

V<- bldiag(remove_zero_mat)

PTSD<- rma.mv<http://rma.mv>(dat$ESP, V, mods= ~ 1, random= list(~ 1| id/nummer), data=dat)

summary (PTSD,digits=3)

In which:
V = the covariance-variance matrices
ESP = the effect size
v1p to v6p = dimensions of the variance-covariance matrices

Thanks in advance.

Kind regards,

Erik van der Meulen





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