I am trying to model the problem below, which is very similar to the example posted here. The sampling runs fine but the traceplot fails with error: `ValueError: cannot convert float NaN to integer`

. The only thing I can think of is that in the trace summary `az.summary(trace, var_names=["~L"],hdi_prob=0.95,round_to=2)`

the `r_hat`

for the first entry of the correlation matrix is NaN. Is there any way to get traceplot to ignore this? or is there something else going wrong here?

Many thanks

```
import numpy as np
import pymc3 as pm
import arviz as az
#generate synthetic data set
n = 500
mu = np.array([0,0])
sigma1 = 2
sigma2 = 1.5
r = -0.7
Sigma = np.reshape([sigma1**2,r*sigma1*sigma2,r*sigma1*sigma2, sigma2**2],(2,2))
D = np.random.multivariate_normal(mu,Sigma,size=n)
#model with pymc3
with pm.Model() as model:
L,R,sigma = pm.LKJCholeskyCov('L', n=2,
eta=1., sd_dist=pm.HalfCauchy.dist(1),compute_corr=True)
mu = pm.Normal('$\mu$',0,1.5,shape=2,testval=0)
cov = pm.Deterministic('$\Sigma$', L.dot(L.T))
likelihood = pm.MvNormal('obs', mu=mu, chol=L, observed=D)
warnings.filterwarnings("ignore")
trace = pm.sample(1000,chains=4,cores=2,progressbar=True,random_seed=1,\
init="adapt_diag")#, return_inferencedata=True)
az.plot_trace(
trace,
var_names=["~L"],
compact=True,
);
```