I'm trying to replicate code from R that estimates a random intercept model. The R code is:
fit=lmer(resid~-1+(1|groupid),data=df)
I'm using the lmer command of the lme4 package to estimate random intercepts for the variable resid for observations in different groups (defined by groupid). There is no 'fixed effects' part, therefore no variable before the (1|groupid). Moreover, I do not want a constant estimated so that I get an intercept for each group.
Not sure how to do similar estimation in Python. I tried something like:
import pandas as pd
import numpy as np
import statsmodels.formula.api as smf
np.random.seed(12345)
df = pd.DataFrame(np.random.randn(25, 4), columns=list('ABCD'))
df['groupid'] = [1,1,1,1,1,2,2,2,2,2,3,3,3,3,3,4,4,4,4,4,5,5,5,5,5]
df['groupid'] = df['groupid'].astype('category')
###Random intercepts models
md = smf.mixedlm('A~B-1',data=df,groups=df['groupid'])
mdf = md.fit()
print(mdf.random_effects)
A is resid from the earlier example, while groupid is the same.
1) I am not sure whether the mdf.random_effects are the random intercepts I am looking for
2) I cannot remove the variable B, which I understand is the fixed effects part. If I try:
md = smf.mixedlm('A~-1',data=df,groups=df['groupid'])
I get an error that "Arrays cannot be empty".
Just trying to estimate the exact same model as in the R code. Any advice will be appreciated.
