Error: The observed covariance matrix is not a positive-definite matrix

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Daria Henning's picture
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Joined: 12/18/2013

Hi all,

I want to fit a one fatcor longitudinal model at three measuremet time points based on an observed covariance/correlation matrix from an published article. Unfortunately I get the following error message when running the obs function, see below.

This is the error:
Error: The observed covariance matrix is not a positive-definite matrix

It would be great if somebody could help with some suggestions!

This is the beginning of the code:

## create observed covariance matrix

obsnames = c("extrT1","beneT1","conscT1","emostT1","imagT1",
"extrT2","beneT2","conscT2","emostT2","imagT2",
"extrT3","beneT3","conscT3","emostT3","imagT3")

obslabels = list (obsnames,obsnames)

values = c(
1.00,
-.05, 1.00,
-.00, .40, 1.00,
.46, .12, .14, 1.00,
.46, .12, .55, .39, 1.00,
.77, -.02, .06, .31, .36, 1.00,
-.15, .72, .28, .02, .01, -.03, 1.00,
-.07, .33, .79, .07, .43, .03, .41, 1.00,
.32, .10, .20, .70, .40, .43, .12, .19, 1.00,
.26, .09, .48, .29, .80, .39, .13, .57, .45, 1.00,
.31, .00, .11, .29, .29, .79, -.01, .07, .33, .24, 1.00,
-.05, .58, .25, -.04, -.01, -.04, .70, .33, .01, .08, .07, 1.00,
.05, .25, .64, -.01, .29, .07, .31, .75, .06, .41, .16, .51, 1.00,
.27, .07, .24, .58, -.04, .32, .05, .20, .66, .38, .44, .13, .14, 1.00,
.31, .09, .45, .29, .58, .34, .14, .51, .39, .79, .43, .22, .55, .49, 1.00)

SD = c(0.43,0.43,0.53,0.51,0.49,0.44,0.39,0.55,0.48,0.50,0.43,0.38,0.54,0.42,0.48)
akkercov = SEM_obsmatrix(lowtri=values,SD=SD,labels=obslabels)

petrmeans = c(3.49,3.50,3.43,3.48,3.69,3.40,3.56,3.41,3.52,3.63,3.32,3.52,3.26,3.52,3.47)
names(petrmeans) = obsnames

factornames = c('PerTrait_1','PerTrait_2','PerTrait_3')
obslabels = list (obsnames, obsnames)
factorlabels = list (factornames, factornames)
lambdalabels = list (obsnames, factornames)
taulabels = list(obsnames,"Intercept")
kappalabels = list (factornames, "Factor mean")

## Fit a longitudinal one-factor model

title = " Factor model Personality traits at 3 timepoints"
obs = mxData(observed = akkercov, type = 'cov', means = petrmeans, numObs = 290)

tbates's picture
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Joined: 07/31/2009
nearest PD matrix

this may help you:

akkercov = Matrix::nearPD(akkercov)$mat
akkercov = as.matrix(akkercov)

http://stat.ethz.ch/R-manual/R-devel/library/Matrix/html/nearPD.html

Daria Henning's picture
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Joined: 12/18/2013
Hi, As I am a real beginner

Hi,

As I am a real beginner to SEM and OpenMX, could you explain what is happening with the function that calculates the nearest covariance matrix?

Daria Henning's picture
Offline
Joined: 12/18/2013
nearest PD matrix

Hi,

I thinks it works now! Thanks a lot!