When building linear mixed models with Python’s statsmodules module, I repeatedly, and often incoherently, ran into np.linalg.LinAlgError errors that are Singular matrix errors.

There are a couple of things to check for with these errors:

First, drop any rows where there are NaN values for the predictors:

e.g. if your predictors are in a list called predictors, try this

df= df.dropna(subset=predictors)

Second, remove any columns whose sum is zero:

to_drop = list(df.loc[:,df.sum(axis=0) <1].columns)
df.drop(columns=to_drop)

Third, now that you have dropped columns, make sure they are still in your predictors. Something like

updated_predictors = list(set(predictors).intersection(set(df.columns)))

Finally, when all that doesn’t work, you should try different methods to fit the model. These are the methods I currently use, and I try them in this order and save the results for the first one that completes.

results = None
for meth in 'bfgs', 'lbfgs', 'cg', 'powell', 'nm':
    try:
        result = model.fit(method=meth)
        print(f"Method {meth} PASSED", file=sys.stderr)
        break
    except np.linalg.LinAlgError as e:
        print(f"Method {meth} failed", file=sys.stderr)
if results:
    print(results.summary)