def test_wls_panel(self):
y = tm.makeTimeDataFrame()
x = Panel({'x1' : tm.makeTimeDataFrame(),
'x2' : tm.makeTimeDataFrame()})
y.ix[[1, 7], 'A'] = np.nan
y.ix[[6, 15], 'B'] = np.nan
y.ix[[3, 20], 'C'] = np.nan
y.ix[[5, 11], 'D'] = np.nan
stack_y = y.stack()
stack_x = DataFrame(dict((k, v.stack())
for k, v in x.iteritems()))
weights = x.std('items')
stack_weights = weights.stack()
stack_y.index = stack_y.index.get_tuple_index()
stack_x.index = stack_x.index.get_tuple_index()
stack_weights.index = stack_weights.index.get_tuple_index()
result = ols(y=y, x=x, weights=1/weights)
expected = ols(y=stack_y, x=stack_x, weights=1/stack_weights)
assert_almost_equal(result.beta, expected.beta)
for attr in ['resid', 'y_fitted']:
rvals = getattr(result, attr).stack().values
evals = getattr(expected, attr).values
assert_almost_equal(rvals, evals)
def test_wls_panel(self):
y = tm.makeTimeDataFrame()
x = Panel({"x1": tm.makeTimeDataFrame(), "x2": tm.makeTimeDataFrame()})
y.ix[[1, 7], "A"] = np.nan
y.ix[[6, 15], "B"] = np.nan
y.ix[[3, 20], "C"] = np.nan
y.ix[[5, 11], "D"] = np.nan
stack_y = y.stack()
stack_x = DataFrame(dict((k, v.stack()) for k, v in compat.iteritems(x)))
weights = x.std("items")
stack_weights = weights.stack()
stack_y.index = stack_y.index._tuple_index
stack_x.index = stack_x.index._tuple_index
stack_weights.index = stack_weights.index._tuple_index
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
result = ols(y=y, x=x, weights=1 / weights)
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
expected = ols(y=stack_y, x=stack_x, weights=1 / stack_weights)
assert_almost_equal(result.beta, expected.beta)
for attr in ["resid", "y_fitted"]:
rvals = getattr(result, attr).stack().values
evals = getattr(expected, attr).values
assert_almost_equal(rvals, evals)
def test_wls_panel(self):
y = tm.makeTimeDataFrame()
x = Panel({'x1': tm.makeTimeDataFrame(),
'x2': tm.makeTimeDataFrame()})
y.iloc[[1, 7], y.columns.get_loc('A')] = np.nan
y.iloc[[6, 15], y.columns.get_loc('B')] = np.nan
y.iloc[[3, 20], y.columns.get_loc('C')] = np.nan
y.iloc[[5, 11], y.columns.get_loc('D')] = np.nan
stack_y = y.stack()
stack_x = DataFrame(dict((k, v.stack())
for k, v in x.iteritems()))
weights = x.std('items')
stack_weights = weights.stack()
stack_y.index = stack_y.index._tuple_index
stack_x.index = stack_x.index._tuple_index
stack_weights.index = stack_weights.index._tuple_index
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
result = ols(y=y, x=x, weights=1 / weights)
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
expected = ols(y=stack_y, x=stack_x, weights=1 / stack_weights)
assert_almost_equal(result.beta, expected.beta)
for attr in ['resid', 'y_fitted']:
rvals = getattr(result, attr).stack().values
evals = getattr(expected, attr).values
assert_almost_equal(rvals, evals)
def test_wls_panel(self):
y = tm.makeTimeDataFrame()
x = Panel({'x1': tm.makeTimeDataFrame(),
'x2': tm.makeTimeDataFrame()})
y.iloc[[1, 7], y.columns.get_loc('A')] = np.nan
y.iloc[[6, 15], y.columns.get_loc('B')] = np.nan
y.iloc[[3, 20], y.columns.get_loc('C')] = np.nan
y.iloc[[5, 11], y.columns.get_loc('D')] = np.nan
stack_y = y.stack()
stack_x = DataFrame(dict((k, v.stack())
for k, v in x.iteritems()))
weights = x.std('items')
stack_weights = weights.stack()
stack_y.index = stack_y.index._tuple_index
stack_x.index = stack_x.index._tuple_index
stack_weights.index = stack_weights.index._tuple_index
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
result = ols(y=y, x=x, weights=1 / weights)
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
expected = ols(y=stack_y, x=stack_x, weights=1 / stack_weights)
assert_almost_equal(result.beta, expected.beta)
for attr in ['resid', 'y_fitted']:
rvals = getattr(result, attr).stack().values
evals = getattr(expected, attr).values
assert_almost_equal(rvals, evals)
def test_wls_panel(self):
y = tm.makeTimeDataFrame()
x = Panel({"x1": tm.makeTimeDataFrame(), "x2": tm.makeTimeDataFrame()})
y.ix[[1, 7], "A"] = np.nan
y.ix[[6, 15], "B"] = np.nan
y.ix[[3, 20], "C"] = np.nan
y.ix[[5, 11], "D"] = np.nan
stack_y = y.stack()
stack_x = DataFrame(dict((k, v.stack()) for k, v in x.iteritems()))
weights = x.std("items")
stack_weights = weights.stack()
stack_y.index = stack_y.index.get_tuple_index()
stack_x.index = stack_x.index.get_tuple_index()
stack_weights.index = stack_weights.index.get_tuple_index()
result = ols(y=y, x=x, weights=1 / weights)
expected = ols(y=stack_y, x=stack_x, weights=1 / stack_weights)
assert_almost_equal(result.beta, expected.beta)
for attr in ["resid", "y_fitted"]:
rvals = getattr(result, attr).stack().values
evals = getattr(expected, attr).values
assert_almost_equal(rvals, evals)
