Is it possible to do glmm in Python (like the GENLINMIXED analysis in SPSS)? I'm a big fan of statsmodels, but this library doesn't seem to support glmm... Are there any alternatives?
-edit-
Decided to do it with R and r2py...
def RunAnalyseMLMlogit(dataset, outcomevars, meeneemvars, randintercept, randslope):
from rpy2.robjects import pandas2ri
from rpy2.robjects.packages import importr
base = importr('base')
stats = importr('stats')
lme4 = importr('lme4')
#data
with SavReaderNp(dataset) as reader_np:
array = reader_np.to_structured_array()
df = pd.DataFrame(array)
variabelen = ' '.join(outcomevars) + ' ~ ' + '+'.join(meeneemvars)
randintercept2 = ['(1|'+i+')' for i in randintercept]
intercept = '+'.join(randintercept2)
randslope2 = ['(1+'+meeneemvars[0]+'|'+i+')' for i in randslope]
slope = ' '.join(randslope2)
pandas2ri.activate()
r_df = pandas2ri.py2ri(df)
#model
#random intercepts + random slopes
if len(randslope) > 0:
formula = variabelen + '+' + intercept + '+' + slope
#only random intercepts
else:
formula = variabelen + '+' + intercept
model = lme4.glmer(formula, data=r_df, family= 'binomial')
resultaat = base.summary(model).rx2('coefficients')
uitkomst = base.summary(model)
return uitkomst
According to this (admittedly, not so recent) post, there still isn't a very good solution to running glmms in Python. However, if you're just looking for a free (and much more flexible!) alternative to running your tests in SPSS, look into the lme4 package for R. You could potentially even use a package such as rpy2, and call R directly from Python, but this might be a little buggy.
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With