I am currently in the process of running a meta analysis using the metaSEM package in OpenMx. As you can see in the first pass at the base model I am getting NA values for Std.Error, lbound, ubound, z value and Pr(>|z|) for both Tau2_2 and Tau2_3. In addition, the OpenMx status1: 5, which I found meant "5: means that the Hessian at the solution is not convex." However I am unclear how to resolve this as a problem. In addition when I run models with both level-2 and level-3 constraints I no longer see this issue, but I don't think it would be appropriate to do model comparisons to a base model that has so many NA values. Any help on how to resolve this issue would be great.

Here is the first few lines of data:

AUTHOR YEAR EXP yFINAL vFINAL typeFINAL

Greenwood 2009 I -1.346327273 0.228655603 G

Greenwood 2009 I 0.220196364 0.224990678 G

Siette 2014 J 0.8974913 0.2625858 MT

Siette 2014 J 1.2197971 0.2732485 MT

Siette 2014 J 0.1487526 0.2503457 MT

> t(aggregate(yFINAL~EXP, data=META_B, FUN=length))

[,1] [,2] [,3] [,4] [,5] [,6] [,7]

EXP "I" "J" "K" "L" "M" "N" "R"

yFINAL " 2" " 3" " 2" " 1" "12" " 2" " 1"

> ## ## Model 0: Random-effects model

> summary( Model0 <- meta3(y=yFINAL, v=vFINAL, cluster=EXP,

+ data=META_B, model.name="3 level") )

Call:

meta3(y = yFINAL, v = vFINAL, cluster = EXP, data = META_B, model.name = "3 level")

95% confidence intervals: z statistic approximation

Coefficients:

Estimate Std.Error lbound ubound z value

Intercept 2.8690e-01 2.2100e-01 -1.4626e-01 7.2005e-01 1.2982

Tau2_2 1.0000e-10 NA NA NA NA

Tau2_3 2.6152e-01 NA NA NA NA

Pr(>|z|)

Intercept 0.1942

Tau2_2 NA

Tau2_3 NA

Q statistic on the homogeneity of effect sizes: 38.8583

Degrees of freedom of the Q statistic: 22

P value of the Q statistic: 0.01464862

Heterogeneity indices (based on the estimated Tau2):

Estimate

I2_2 (Typical v: Q statistic) 0.0000

I2_3 (Typical v: Q statistic) 0.5675

Number of studies (or clusters): 7

Number of observed statistics: 23

Number of estimated parameters: 3

Degrees of freedom: 20

-2 log likelihood: 40.06405

OpenMx status1: 5 ("0" or "1": The optimization is considered fine.

Other values may indicate problems.)

> ## ## Model 1: Testing tau^2_3 = 0

> Model1 <- meta3(y=yFINAL, v=vFINAL, cluster=EXP,

+ data=META_B,

+ RE3.constraints=0, model.name="2 level")

Call:

meta3(y = yFINAL, v = vFINAL, cluster = EXP, data = META_B, RE3.constraints = 0,

model.name = "2 level")

95% confidence intervals: z statistic approximation

Coefficients:

Estimate Std.Error lbound ubound z value Pr(>|z|)

Intercept 0.055543 0.119228 -0.178139 0.289224 0.4659 0.6413

Tau2_2 0.102482 0.113505 -0.119984 0.324948 0.9029 0.3666

Q statistic on the homogeneity of effect sizes: 38.8583

Degrees of freedom of the Q statistic: 22

P value of the Q statistic: 0.01464862

Heterogeneity indices (based on the estimated Tau2):

Estimate

I2_2 (Typical v: Q statistic) 0.3396

I2_3 (Typical v: Q statistic) 0.0000

Number of studies (or clusters): 7

Number of observed statistics: 23

Number of estimated parameters: 2

Degrees of freedom: 21

-2 log likelihood: 45.00194

OpenMx status1: 0 ("0" or "1": The optimization is considered fine.

Other values may indicate problems.)

> Model2 <- meta3(y=yFINAL, v=vFINAL, cluster=EXP,

+ data=META_B,

+ RE2.constraints=0, model.name="tau2_2 EQ 0")

> summary(Model2)

Call:

meta3(y = yFINAL, v = vFINAL, cluster = EXP, data = META_B, RE2.constraints = 0,

model.name = "tau2_2 EQ 0")

95% confidence intervals: z statistic approximation

Coefficients:

Estimate Std.Error lbound ubound z value Pr(>|z|)

Intercept 0.28690 0.25058 -0.20422 0.77801 1.1449 0.2522

Tau2_3 0.26152 0.24783 -0.22423 0.74726 1.0552 0.2913

Q statistic on the homogeneity of effect sizes: 38.8583

Degrees of freedom of the Q statistic: 22

P value of the Q statistic: 0.01464862

Heterogeneity indices (based on the estimated Tau2):

Estimate

I2_2 (Typical v: Q statistic) 0.0000

I2_3 (Typical v: Q statistic) 0.5675

Number of studies (or clusters): 7

Number of observed statistics: 23

Number of estimated parameters: 2

Degrees of freedom: 21

-2 log likelihood: 40.06405

OpenMx status1: 0 ("0" or "1": The optimization is considered fine.

Other values may indicate problems.)

The R code does not seem to be valid R code. For example,

summary( Model0 + data=META_B, model.name="3 level") )

Model2 + data=META_B, RE2.constraints=0, model.name="tau2_2 EQ 0")

Could you please posting the data and the R code?

I am not 100% sure how to attach the full file with my data but here are the first 5 rows from the data set:

AUTHOR YEAR EXP yFINAL vFINAL typeFINAL

Greenwood 2009 I -1.346327273 0.228655603 G

Greenwood 2009 I 0.220196364 0.224990678 G

Siette 2014 J 0.8974913 0.2625858 MT

Siette 2014 J 1.2197971 0.2732485 MT

Siette 2014 J 0.1487526 0.2503457 MT

Below is the code I ran to get the output above. Not 100% sure where the "Model2 + data=META_B" came from when I copied it here, however I don't see it in my output now. So sorry.

library("metaSEM")

t(aggregate(yFINAL~EXP, data=META_B, FUN=length)

## ## Model 0: Random-effects model

summary( Model0 <- meta3(y=yFINAL, v=vFINAL, cluster=EXP, data=META_B, model.name="3 level") )

## ## Model 0: Random-effects model with LBCI

summary( meta3(y=yFINAL, v=vFINAL, cluster=EXP,

data=META_B, model.name="3 level",

intervals.type="LB") )

## ## Model 1: Testing tau^2_3 = 0

Model1 <- meta3(y=yFINAL, v=vFINAL, cluster=EXP,

data=META_B,

RE3.constraints=0, model.name="2 level")

summary(Model1)

anova(Model0, Model1)

## ## Model 2: Testing tau^2_2 = 0

Model2 <- meta3(y=yFINAL, v=vFINAL, cluster=EXP,

data=META_B,

RE2.constraints=0, model.name="tau2_2 EQ 0")

anova(Model0, Model2)

## ## Model 3: Testing tau^2_2 = tau^2_3

Model3 <- meta3(y=yFINAL, v=vFINAL, cluster=EXP,

data=META_B,

RE2.constraints="0.1

Eq_tau2",Eq_tau2",RE3.constraints="0.1

model.name="Eq tau2")

anova(Model0, Model3)

Ok is there any way to attach a file, for some reason everything looks good on y end when I try and post, then characters disappear? I am trying one last time to post the code for my base model only. I am so sorry this is so confusing.

## ## Model 0: Random-effects model

summary( Model0 <-

meta3(y=yG, v=vG, cluster=EXP,

data=META, model.name="3 level") )

What was happening right after we switched to our new website was that Drupal was interpreting the main assignment operator in R,

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In sending code, it may help to put

`at the beginning and`

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