I've encountered a problem with a MI impute code. The mIssing data table is below:
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misstable patterns log_accessions pct_maori deprivationindex log_level6 pctbachelors pctmasters pctdoctorate turnover unemploymentrate15_oecd log_minority pct_allminorities log_eqcfunding log_ceraassistance Missing-value patterns (1 means complete) | Pattern Percent | 1 2 3 4 5 6 7 8 9 10 11 12 13 ------------+---------------------------------------------- <1% | 1 1 1 1 1 1 1 1 1 1 1 1 1 | 61 | 1 1 1 0 0 0 0 0 0 0 0 0 0 14 | 1 1 1 1 1 1 1 1 1 1 1 0 0 11 | 1 0 0 0 0 0 0 0 0 0 0 0 0 7 | 0 1 1 1 0 0 0 0 0 0 0 0 0 2 | 1 1 1 0 0 0 0 0 0 0 0 1 1 1 | 0 0 0 1 0 0 0 0 0 0 0 0 0 1 | 1 0 0 1 0 0 0 0 0 0 0 0 0 1 | 1 0 0 1 1 1 1 1 1 1 1 0 0 <1 | 1 1 1 0 0 0 0 0 0 0 0 1 0 ------------+---------------------------------------------- 100% | Variables are (1) unemploymentrate15_oecd (2) log_accessions (3) turnover (4) deprivationindex (5) log_level6 (6) log_minority (7) pct_allminorities (8) pct_maori (9) pctbachelors (10) pctdoctorate (11) pctmasters (12) log_ceraassistance (13) log_eqcfunding
Code:
mi impute mvn log_accessions pct_maori deprivationindex log_level6 pctbachelors pctmasters pctdoctorate turnover unemploymentrate15_oecd log_minority pct_allminorities log_eqcfunding log_ceraassistance, burnin(1000) add(20) rseed (1234)
observed log likelihood = -295.64702 at iteration 100
(EM did not converge)
Performing MCMC data augmentation ...
Iteration 1601: variance-covariance matrix (Sigma) became not positive definite
posterior distribution is not proper
error occurred during imputation of log_accessions pct_maori deprivationindex log_level6 pctbachelors pctmasters pctdoctorate turnover unemploymentrate15_oecd log_minority pct_allminorities
log_eqcfunding log_ceraassistance on m = 8
I have no idea how to fix this. I've read the r(498) message but it isn't very informative. Any assistance would be greatly appreciated.
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