@@ -271,36 +271,36 @@ def compare(traces, models, ic='WAIC'):
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Parameters
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----------
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- traces: list of PyMC3 traces
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- models: list of PyMC3 models
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+ traces : list of PyMC3 traces
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+ models : list of PyMC3 models
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in the same order as traces.
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- ic: string
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+ ic : string
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Information Criterion (WAIC or LOO) used to compare models.
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Default WAIC.
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Returns
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-------
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A DataFrame, ordered from lowest to highest IC. The index reflects
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the order in which the models are passed to this function. The columns are:
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- IC: Information Criteria (WAIC or LOO).
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+ IC : Information Criteria (WAIC or LOO).
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Smaller IC indicates higher out-of-sample predictive fit ("better" model).
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Default WAIC.
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- pIC: Estimated effective number of parameters.
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- dIC: Relative difference between each IC (WAIC or LOO)
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+ pIC : Estimated effective number of parameters.
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+ dIC : Relative difference between each IC (WAIC or LOO)
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and the lowest IC (WAIC or LOO).
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It's always 0 for the top-ranked model.
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weight: Akaike weights for each model.
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This can be loosely interpreted as the probability of each model
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(among the compared model) given the data. Be careful that these
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weights are based on point estimates of the IC (uncertainty is ignored).
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- SE: Standard error of the IC estimate.
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+ SE : Standard error of the IC estimate.
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For a "large enough" sample size this is an estimate of the uncertainty
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in the computation of the IC.
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- dSE: Standard error of the difference in IC between each model and
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+ dSE : Standard error of the difference in IC between each model and
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the top-ranked model.
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It's always 0 for the top-ranked model.
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- warning: A value of 1 indicates that the computation of the IC may not be
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- reliable see http://arxiv.org/abs/1507.04544 for details
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+ warning : A value of 1 indicates that the computation of the IC may not be
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+ reliable see http://arxiv.org/abs/1507.04544 for details.
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"""
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if ic == 'WAIC' :
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ic_func = waic
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