Hi Jamie,
Thank you so much for the fast reply!
Running the lme-regression simplified with --p formula “Size” worked, the coefficients summary doesn’t look too promising tho.
Running the lme-regression with the verbose flag gives the following errors - I scanned the whole report and shortened it here, since these lines are repeating:
/home/qiime/anaconda3/envs/qiime2/lib/python3.5/site-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
"Check mle_retvals", ConvergenceWarning)
/home/qiime/anaconda3/envs/qiime2/lib/python3.5/site-packages/statsmodels/regression/mixed_linear_model.py:2001: ConvergenceWarning: Gradient optimization failed.
warnings.warn(msg, ConvergenceWarning)
/home/qiime/anaconda3/envs/qiime2/lib/python3.5/site-packages/statsmodels/regression/mixed_linear_model.py:2019: ConvergenceWarning: The MLE may be on the boundary of the parameter space.
/home/qiime/anaconda3/envs/qiime2/lib/python3.5/site-packages/statsmodels/regression/mixed_linear_model.py:2039: ConvergenceWarning: The Hessian matrix at the estimated parameter values is not positive definite.
warnings.warn(msg, ConvergenceWarning)
/home/qiime/anaconda3/envs/qiime2/lib/python3.5/site-packages/statsmodels/base/model.py:1029: RuntimeWarning: invalid value encountered in sqrt
return np.sqrt(np.diag(self.cov_params()))
/home/qiime/anaconda3/envs/qiime2/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater
return (self.a < x) & (x < self.b)
/home/qiime/anaconda3/envs/qiime2/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less
return (self.a < x) & (x < self.b)
/home/qiime/anaconda3/envs/qiime2/lib/python3.5/site-packages/scipy/stats/_distn_infrastructure.py:1818: RuntimeWarning: invalid value encountered in less_equal
cond2 = cond0 & (x <= self.a)
/home/qiime/anaconda3/envs/qiime2/lib/python3.5/site-packages/statsmodels/stats/multitest.py:320: RuntimeWarning: invalid value encountered in less_equal
reject = pvals_sorted <= ecdffactor*alpha
Traceback (most recent call last):
File “/home/qiime/anaconda3/envs/qiime2/lib/python3.5/site-packages/q2cli/commands.py”, line 224, in call
results = action(**arguments)
File “”, line 2, in lme_regression
File “/home/qiime/anaconda3/envs/qiime2/lib/python3.5/site-packages/qiime2/sdk/action.py”, line 228, in bound_callable
output_types, provenance)
File “/home/qiime/anaconda3/envs/qiime2/lib/python3.5/site-packages/qiime2/sdk/action.py”, line 424, in callable_executor
ret_val = self._callable(output_dir=temp_dir, **view_args)
File “/home/qiime/anaconda3/envs/qiime2/lib/python3.5/site-packages/q2_gneiss/regression/_regression.py”, line 73, in lme_regression
lme_summary(output_dir, res, tree)
File “/home/qiime/anaconda3/envs/qiime2/lib/python3.5/site-packages/gneiss-0.4.2-py3.5.egg/gneiss/plot/_regression_plot.py”, line 357, in lme_summary
plot_width=900, plot_height=400)
File “/home/qiime/anaconda3/envs/qiime2/lib/python3.5/site-packages/gneiss-0.4.2-py3.5.egg/gneiss/plot/_regression_plot.py”, line 186, in _heatmap_summary
ind = int(np.floor((x - _min) / (_max - _min) * (N - 1)))
ValueError: cannot convert float NaN to integer
Do I interpret correctly, this means, there just isn’t enough variation in the data, apart from the variation caused by my random effects (Site)?
Some more information on my data:
2539 taxa and 120 samples
Fixed effects:
Size - categorical variable with two levels
Al, Cr, Cu, Zn, As - continuous variables, values were clr-transformed
Random effects:
Site - categorical variable with ten levels.
Thank you for your help!
Lena