class TestRlm(CheckRlmResults):
from scikits.statsmodels.datasets.stackloss import load
data = load() # class attributes for subclasses
data.exog = sm.add_constant(data.exog)
def __init__(self):
# Test precisions
self.decimal_standarderrors = DECIMAL_1
self.decimal_scale = DECIMAL_3
results = RLM(self.data.endog, self.data.exog,\
M=sm.robust.norms.HuberT()).fit() # default M
h2 = RLM(self.data.endog, self.data.exog,\
M=sm.robust.norms.HuberT()).fit(cov="H2").bcov_scaled
h3 = RLM(self.data.endog, self.data.exog,\
M=sm.robust.norms.HuberT()).fit(cov="H3").bcov_scaled
self.res1 = results
self.res1.h2 = h2
self.res1.h3 = h3
def setup(self):
# r.library('MASS')
# self.res2 = RModel(self.data.endog, self.data.exog,
# r.rlm, psi="psi.huber")
from results.results_rlm import Huber
self.res2 = Huber()
class TestRlmHuber(CheckRlmResults):
from scikits.statsmodels.datasets.stackloss import load
data = load()
data.exog = sm.add_constant(data.exog)
def __init__(self):
results = RLM(self.data.endog, self.data.exog,\
M=sm.robust.norms.HuberT()).fit(scale_est=\
sm.robust.scale.HuberScale())
h2 = RLM(self.data.endog, self.data.exog,\
M=sm.robust.norms.HuberT()).fit(cov="H2",
scale_est=sm.robust.scale.HuberScale()).bcov_scaled
h3 = RLM(self.data.endog, self.data.exog,\
M=sm.robust.norms.HuberT()).fit(cov="H3",
scale_est=sm.robust.scale.HuberScale()).bcov_scaled
self.res1 = results
self.res1.h2 = h2
self.res1.h3 = h3
def setup(self):
from results.results_rlm import HuberHuber
self.res2 = HuberHuber()
""" import numpy as np import scikits.statsmodels.api as sm RLM = sm.RLM DECIMAL_4 = 4 DECIMAL_3 = 3 DECIMAL_2 = 2 DECIMAL_1 = 1 from scikits.statsmodels.datasets.stackloss import load data = load() # class attributes for subclasses data.exog = sm.add_constant(data.exog) decimal_standarderrors = DECIMAL_1 decimal_scale = DECIMAL_3 results = RLM(data.endog, data.exog, M=sm.robust.norms.HuberT()).fit() # default M h2 = RLM(data.endog, data.exog, M=sm.robust.norms.HuberT()).fit(cov="H2").bcov_scaled h3 = RLM(data.endog, data.exog, M=sm.robust.norms.HuberT()).fit(cov="H3").bcov_scaled from scikits.statsmodels.robust.tests.results.results_rlm import Huber res2 = Huber() print "res2.h1"
# results_huber = model_huber.fit(scale_est="stand_mad", update_scale=False)
# model_ramsaysE = RLM(endog, exog, M=norms.RamsayE())
# results_ramsaysE = model_ramsaysE.fit(update_scale=False)
# model_andrewWave = RLM(endog, exog, M=norms.AndrewWave())
# results_andrewWave = model_andrewWave.fit(update_scale=False)
# model_hampel = RLM(endog, exog, M=norms.Hampel(a=1.7,b=3.4,c=8.5)) # convergence problems with scale changed, not with 2,4,8 though?
# results_hampel = model_hampel.fit(update_scale=False)
#######################
### Stack Loss Data ###
#######################
from scikits.statsmodels.datasets.stackloss import load
data = load()
data.exog = sm.add_constant(data.exog)
#############
### Huber ###
#############
# m1_Huber = RLM(data.endog, data.exog, M=norms.HuberT())
# results_Huber1 = m1_Huber.fit()
# m2_Huber = RLM(data.endog, data.exog, M=norms.HuberT())
# results_Huber2 = m2_Huber.fit(cov="H2")
# m3_Huber = RLM(data.endog, data.exog, M=norms.HuberT())
# results_Huber3 = m3_Huber.fit(cov="H3")
##############
### Hampel ###
##############
# m1_Hampel = RLM(data.endog, data.exog, M=norms.Hampel())
# results_Hampel1 = m1_Hampel.fit()
from scikits.statsmodels.sandbox.tools import cross_val
if __name__ == '__main__':
#A: josef-pktd
import scikits.statsmodels.api as sm
from scikits.statsmodels.api import OLS
#from scikits.statsmodels.datasets.longley import load
from scikits.statsmodels.datasets.stackloss import load
from scikits.statsmodels.iolib.table import (SimpleTable, default_txt_fmt,
default_latex_fmt,
default_html_fmt)
import numpy as np
data = load()
data.exog = sm.tools.add_constant(data.exog)
resols = sm.OLS(data.endog, data.exog).fit()
print '\n OLS leave 1 out'
for inidx, outidx in cross_val.LeaveOneOut(len(data.endog)):
res = sm.OLS(data.endog[inidx], data.exog[inidx, :]).fit()
print data.endog[outidx], res.model.predict(res.params,
data.exog[outidx, :]),
print data.endog[outidx] - res.model.predict(res.params,
data.exog[outidx, :])
print '\n OLS leave 2 out'
resparams = []
for inidx, outidx in cross_val.LeavePOut(len(data.endog), 2):
Created on Sun Mar 27 14:36:40 2011
"""
import numpy as np
import scikits.statsmodels.api as sm
RLM = sm.RLM
DECIMAL_4 = 4
DECIMAL_3 = 3
DECIMAL_2 = 2
DECIMAL_1 = 1
from scikits.statsmodels.datasets.stackloss import load
data = load() # class attributes for subclasses
data.exog = sm.add_constant(data.exog)
decimal_standarderrors = DECIMAL_1
decimal_scale = DECIMAL_3
results = RLM(data.endog, data.exog,\
M=sm.robust.norms.HuberT()).fit() # default M
h2 = RLM(data.endog, data.exog,\
M=sm.robust.norms.HuberT()).fit(cov="H2").bcov_scaled
h3 = RLM(data.endog, data.exog,\
M=sm.robust.norms.HuberT()).fit(cov="H3").bcov_scaled
from scikits.statsmodels.robust.tests.results.results_rlm import Huber
res2 = Huber()
