AIM-PIbd-32-Kurbanova-A-A/aimenv/Lib/site-packages/statsmodels/datasets/randhie/data.py
2024-10-02 22:15:59 +04:00

86 lines
2.2 KiB
Python

"""RAND Health Insurance Experiment Data"""
from statsmodels.datasets import utils as du
__docformat__ = 'restructuredtext'
COPYRIGHT = """This is in the public domain."""
TITLE = __doc__
SOURCE = """
The data was collected by the RAND corporation as part of the Health
Insurance Experiment (HIE).
http://www.rand.org/health/projects/hie.html
This data was used in::
Cameron, A.C. amd Trivedi, P.K. 2005. `Microeconometrics: Methods
and Applications,` Cambridge: New York.
And was obtained from: <http://cameron.econ.ucdavis.edu/mmabook/mmadata.html>
See randhie/src for the original data and description. The data included
here contains only a subset of the original data. The data varies slightly
compared to that reported in Cameron and Trivedi.
"""
DESCRSHORT = """The RAND Co. Health Insurance Experiment Data"""
DESCRLONG = """"""
NOTE = """::
Number of observations - 20,190
Number of variables - 10
Variable name definitions::
mdvis - Number of outpatient visits to an MD
lncoins - ln(coinsurance + 1), 0 <= coninsurance <= 100
idp - 1 if individual deductible plan, 0 otherwise
lpi - ln(max(1, annual participation incentive payment))
fmde - 0 if idp = 1; ln(max(1, MDE/(0.01 coinsurance))) otherwise
physlm - 1 if the person has a physical limitation
disea - number of chronic diseases
hlthg - 1 if self-rated health is good
hlthf - 1 if self-rated health is fair
hlthp - 1 if self-rated health is poor
(Omitted category is excellent self-rated health)
"""
def load():
"""
Loads the RAND HIE data and returns a Dataset class.
Returns
-------
Dataset
See DATASET_PROPOSAL.txt for more information.
Notes
-----
endog - response variable, mdvis
exog - design
"""
return load_pandas()
def load_pandas():
"""
Loads the RAND HIE data and returns a Dataset class.
Returns
-------
Dataset
See DATASET_PROPOSAL.txt for more information.
Notes
-----
endog - response variable, mdvis
exog - design
"""
return du.process_pandas(_get_data(), endog_idx=0)
def _get_data():
return du.load_csv(__file__, 'randhie.csv')