70 lines
2.3 KiB
Python
70 lines
2.3 KiB
Python
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"""First 100 days of the US House of Representatives 1995"""
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from statsmodels.datasets import utils as du
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__docformat__ = 'restructuredtext'
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COPYRIGHT = """Used with express permission from the original author,
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who retains all rights."""
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TITLE = __doc__
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SOURCE = """
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Jeff Gill's `Generalized Linear Models: A Unifited Approach`
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http://jgill.wustl.edu/research/books.html
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"""
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DESCRSHORT = """Number of bill assignments in the 104th House in 1995"""
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DESCRLONG = """The example in Gill, seeks to explain the number of bill
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assignments in the first 100 days of the US' 104th House of Representatives.
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The response variable is the number of bill assignments in the first 100 days
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over 20 Committees. The explanatory variables in the example are the number of
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assignments in the first 100 days of the 103rd House, the number of members on
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the committee, the number of subcommittees, the log of the number of staff
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assigned to the committee, a dummy variable indicating whether
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the committee is a high prestige committee, and an interaction term between
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the number of subcommittees and the log of the staff size.
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The data returned by load are not cleaned to represent the above example.
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"""
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NOTE = """::
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Number of Observations - 20
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Number of Variables - 6
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Variable name definitions::
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BILLS104 - Number of bill assignments in the first 100 days of the
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104th House of Representatives.
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SIZE - Number of members on the committee.
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SUBS - Number of subcommittees.
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STAFF - Number of staff members assigned to the committee.
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PRESTIGE - PRESTIGE == 1 is a high prestige committee.
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BILLS103 - Number of bill assignments in the first 100 days of the
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103rd House of Representatives.
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Committee names are included as a variable in the data file though not
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returned by load.
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"""
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def load_pandas():
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data = _get_data()
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return du.process_pandas(data, endog_idx=0)
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def load():
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"""Load the committee data and returns a data class.
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Returns
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-------
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Dataset
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See DATASET_PROPOSAL.txt for more information.
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"""
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return load_pandas()
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def _get_data():
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data = du.load_csv(__file__, 'committee.csv')
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data = data.iloc[:, 1:7].astype(float)
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return data
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