def cov_cluster_2groups(results, group, group2=None, use_correction=True):
'''cluster robust covariance matrix for two groups/clusters
Parameters
----------
results : result instance
result of a regression, uses results.model.exog and results.resid
TODO: this should use wexog instead
use_correction : bool
If true (default), then the small sample correction factor is used.
Returns
-------
cov_both : ndarray, (k_vars, k_vars)
cluster robust covariance matrix for parameter estimates, for both
clusters
cov_0 : ndarray, (k_vars, k_vars)
cluster robust covariance matrix for parameter estimates for first
cluster
cov_1 : ndarray, (k_vars, k_vars)
cluster robust covariance matrix for parameter estimates for second
cluster
Notes
-----
verified against Peterson's table, (4 decimal print precision)
'''
if group2 is None:
if group.ndim != 2 or group.shape[1] != 2:
raise ValueError(
'if group2 is not given, then groups needs to be ' +
'an array with two columns')
group0 = group[:, 0]
group1 = group[:, 1]
else:
group0 = group
group1 = group2
group = (group0, group1)
cov0 = cov_cluster(results, group0, use_correction=use_correction)[0]
#[0] because we get still also returns bse
cov1 = cov_cluster(results, group1, use_correction=use_correction)[0]
group_intersection = Group(group)
#cov of cluster formed by intersection of two groups
cov01 = cov_cluster(results,
group_intersection.group_int,
use_correction=use_correction)[0]
#robust cov matrix for union of groups
cov_both = cov0 + cov1 - cov01
#return all three (for now?)
return cov_both, cov0, cov1
def test_group_class():
# Moved from grouputils __main__ section
g = np.array([0, 0, 1, 2, 1, 1, 2, 0])
x = np.arange(len(g)*3).reshape(len(g), 3, order='F')
mygroup = Group(g)
mygroup.group_int
mygroup.group_sums(x)
mygroup.labels()