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OpenMX Status: 5

Posted on
No user picture. sorin.valcea Joined: 05/31/2017
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metaSEM
Hello all,

I am getting status 5 for some analyses and am not sure why. I am including the code and output below. There are 4 clusters and 9 effects. Any idea what is causing it?

Thanks!

########## CODE ##########
clustvar <-c(3.1,3.1,41.1,52.1,52.1,52.1,52.1,56.1,56.1)
effsize <-c(0.0370169,-0.04603249,0.25541281,0.20065009,0.18405043,0.12667322,0.21526454,0.01300073,-0.05304971)
samplvar <-c(0.01020408,0.01020408,0.01298701,0.01538462,0.01538462,0.01538462,0.01538462,0.0078125,0.0078125)
summary( meta3(y=effsize, v=samplvar,cluster=clustvar) )

######### End Code ##########

##### Output below #####
Call:
meta3(y = effsize, v = samplvar, cluster = clustvar)

95% confidence intervals: z statistic approximation
Coefficients:
Estimate Std.Error lbound ubound z value Pr(>|z|)
Intercept 8.5684e-02 5.5208e-02 -2.2522e-02 1.9389e-01 1.5520 0.1207
Tau2_2 1.4867e-09 NA NA NA NA NA
Tau2_3 6.3242e-03 6.6531e-03 -6.7157e-03 1.9364e-02 0.9506 0.3418

Q statistic on the homogeneity of effect sizes: 9.921885 Degrees of freedom of the Q statistic: 8 P value of the Q statistic: 0.2705515

Heterogeneity indices (based on the estimated Tau2):
Estimate
I2_2 (Typical v: Q statistic) 0.0000
I2_3 (Typical v: Q statistic) 0.3543

Number of studies (or clusters): 4
Number of observed statistics: 9
Number of estimated parameters: 3
Degrees of freedom: 6
-2 log likelihood: -15.12369
OpenMx status1: 5 ("0" or "1": The optimization is considered fine.
Other values may indicate problems.)

Replied on Fri, 06/02/2017 - 22:11
Picture of user. Mike Cheung Joined: 10/08/2009

Dear Sorin,

I think that the issue is related to the small numbers of studies and effect sizes:

## Number of effect sizes per cluster
sapply(split(effsize, clustvar), length)
## 3.1 41.1 52.1 56.1
## 2 1 4 2

If you rerun it, the results seem fine. Tau2_2 is amost zero indicating that there is not much variation within studies.

fit1 <- meta3(y=effsize, v=samplvar,cluster=clustvar)
fit1 <- rerun(fit1, extraTries=50)
summary(fit1)
##
## Call:
## meta3(y = effsize, v = samplvar, cluster = clustvar)
##
## 95% confidence intervals: z statistic approximation
## Coefficients:
## Estimate Std.Error lbound ubound z value Pr(>|z|)
## Intercept 8.5683e-02 5.5208e-02 -2.2522e-02 1.9389e-01 1.5520 0.1207
## Tau2_2 1.0000e-10 NA NA NA NA NA
## Tau2_3 6.3242e-03 6.6531e-03 -6.7157e-03 1.9364e-02 0.9506 0.3418
##
## Q statistic on the homogeneity of effect sizes: 9.921885
## Degrees of freedom of the Q statistic: 8
## P value of the Q statistic: 0.2705515
##
## Heterogeneity indices (based on the estimated Tau2):
## Estimate
## I2_2 (Typical v: Q statistic) 0.0000
## I2_3 (Typical v: Q statistic) 0.3543
##
## Number of studies (or clusters): 4
## Number of observed statistics: 9
## Number of estimated parameters: 3
## Degrees of freedom: 6
## -2 log likelihood: -15.12369
## OpenMx status1: 0 ("0" or "1": The optimization is considered fine.
## Other values may indicate problems.)

Since there are only a few studies (and effect sizes), the findings are highly questionable.

Mike

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