In [2]:
x
import statsmodels.api as sm
In [3]:
 
import statsmodels.formula.api as smf
In [4]:
 
data = sm.datasets.get_rdataset("dietox", "geepack").data
In [18]:
xxxxxxxxxx
 
training = data.iloc[0:500,]
testing = data.iloc[501:-1,]["Time"].to_frame()
In [9]:
md = smf.mixedlm("Weight ~ Time", training, groups=training["Pig"])
In [10]:
mdf = md.fit()
In [20]:
 
mdf.predict(testing)
---------------------------------------------------------------------------
NotImplementedError                       Traceback (most recent call last)
<ipython-input-20-38abca506908> in <module>()
----> 1 mdf.predict(testing)

/Users/wangya/anaconda/lib/python2.7/site-packages/statsmodels/base/model.pyc in predict(self, exog, transform, *args, **kwargs)
    747             exog = np.atleast_2d(exog)  # needed in count model shape[1]
    748 
--> 749         return self.model.predict(self.params, exog, *args, **kwargs)
    750 
    751 

/Users/wangya/anaconda/lib/python2.7/site-packages/statsmodels/base/model.pyc in predict(self, params, exog, *args, **kwargs)
    175         This is a placeholder intended to be overwritten by individual models.
    176         """
--> 177         raise NotImplementedError
    178 
    179 

NotImplementedError: 

In [11]:
x
print(mdf.summary())
          Mixed Linear Model Regression Results
========================================================
Model:            MixedLM Dependent Variable: Weight    
No. Observations: 500     Method:             REML      
No. Groups:       42      Scale:              9.6607    
Min. group size:  10      Likelihood:         -1351.6411
Max. group size:  12      Converged:          Yes       
Mean group size:  11.9                                  
--------------------------------------------------------
             Coef.  Std.Err.    z    P>|z| [0.025 0.975]
--------------------------------------------------------
Intercept    14.905    0.862  17.296 0.000 13.216 16.594
Time          6.969    0.041 171.925 0.000  6.889  7.048
Intercept RE 27.506    2.129                            
========================================================

In [19]:
testing
Out[19]:
Time
501 12
502 1
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504 3
505 4
506 5
507 6
508 7
509 8
510 9
511 10
512 11
513 12
514 1
515 2
516 3
517 4
518 5
519 6
520 7
521 8
522 9
523 10
524 11
525 12
526 1
527 2
528 3
529 4
530 5
... ...
830 6
831 7
832 8
833 9
834 10
835 11
836 12
837 1
838 2
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840 4
841 5
842 6
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844 8
845 9
846 10
847 11
848 12
849 1
850 2
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852 4
853 5
854 6
855 7
856 8
857 9
858 10
859 11

359 rows × 1 columns

In [19]:
 
mdf.predict(data["Time"])
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-19-cbc33a4b31b4> in <module>()
----> 1 mdf.predict(data["Time"])

/Users/wangya/anaconda/lib/python2.7/site-packages/statsmodels/base/model.py in predict(self, exog, transform, *args, **kwargs)
    738             from patsy import dmatrix
    739             exog = dmatrix(self.model.data.orig_exog.design_info.builder,
--> 740                            exog)
    741 
    742         if exog is not None:

/Users/wangya/anaconda/lib/python2.7/site-packages/patsy/highlevel.py in dmatrix(formula_like, data, eval_env, NA_action, return_type)
    289     eval_env = EvalEnvironment.capture(eval_env, reference=1)
    290     (lhs, rhs) = _do_highlevel_design(formula_like, data, eval_env,
--> 291                                       NA_action, return_type)
    292     if lhs.shape[1] != 0:
    293         raise PatsyError("encountered outcome variables for a model "

/Users/wangya/anaconda/lib/python2.7/site-packages/patsy/highlevel.py in _do_highlevel_design(formula_like, data, eval_env, NA_action, return_type)
    167         return build_design_matrices(design_infos, data,
    168                                      NA_action=NA_action,
--> 169                                      return_type=return_type)
    170     else:
    171         # No builders, but maybe we can still get matrices

/Users/wangya/anaconda/lib/python2.7/site-packages/patsy/build.py in build_design_matrices(design_infos, data, NA_action, return_type, dtype)
    886         for factor_info in six.itervalues(design_info.factor_infos):
    887             if factor_info not in factor_info_to_values:
--> 888                 value, is_NA = _eval_factor(factor_info, data, NA_action)
    889                 factor_info_to_isNAs[factor_info] = is_NA
    890                 # value may now be a Series, DataFrame, or ndarray

/Users/wangya/anaconda/lib/python2.7/site-packages/patsy/build.py in _eval_factor(factor_info, data, NA_action)
     61 def _eval_factor(factor_info, data, NA_action):
     62     factor = factor_info.factor
---> 63     result = factor.eval(factor_info.state, data)
     64     # Returns either a 2d ndarray, or a DataFrame, plus is_NA mask
     65     if factor_info.type == "numerical":

/Users/wangya/anaconda/lib/python2.7/site-packages/patsy/eval.py in eval(self, memorize_state, data)
    564         return self._eval(memorize_state["eval_code"],
    565                           memorize_state,
--> 566                           data)
    567 
    568     __getstate__ = no_pickling

/Users/wangya/anaconda/lib/python2.7/site-packages/patsy/eval.py in _eval(self, code, memorize_state, data)
    549                                  memorize_state["eval_env"].eval,
    550                                  code,
--> 551                                  inner_namespace=inner_namespace)
    552 
    553     def memorize_chunk(self, state, which_pass, data):

/Users/wangya/anaconda/lib/python2.7/site-packages/patsy/compat.py in call_and_wrap_exc(msg, origin, f, *args, **kwargs)
    115 def call_and_wrap_exc(msg, origin, f, *args, **kwargs):
    116     try:
--> 117         return f(*args, **kwargs)
    118     except Exception as e:
    119         if sys.version_info[0] >= 3:

/Users/wangya/anaconda/lib/python2.7/site-packages/patsy/eval.py in eval(self, expr, source_name, inner_namespace)
    164         code = compile(expr, source_name, "eval", self.flags, False)
    165         return eval(code, {}, VarLookupDict([inner_namespace]
--> 166                                             + self._namespaces))
    167 
    168     @classmethod

<string> in <module>()

NameError: name 'Time' is not defined

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