gelman_rubin returns wrong values

[isofer]

x = pm.Normal('x',0,1,size=5)

@pm.deterministic
def y(x=x):
return x[2]

m = pm.MCMC([y,x])
m.sample(100,0)
m.sample(100,0)
m.sample(100,0)
pm.diagnostics.gelman_rubin(m)

---

I ran this code and got different values for y and for x[2].
for instance,
{'x': [0.96274904628327884,
1.0617412110342699,
0.94988095858687061,
0.94650618423398925,
1.0581700972182297],
'y': 1.0180891431696331}

[fonnesbeck]

Its not clear to me what the bug is here. The G-R statistics should be approximately one if convergence is reached. They are based on a stochastic sample, so you should not expect them to be identical each time you run the code. If I am missing something, please clarify.

[isofer]

y is a deterministic node and it's equal to x[2], so the trace of y and the trace of x[2] is identical,
which means that GR of x[2] should be equal to the GR of y.
In the result above you can see that x[2] has GR of 0.949880...
while the GR of y is 1.01808

[fonnesbeck]

Got it, thanks. It was just a silly transpose error. Fixed in 81999ab