def fit(self, exog_predict=None, exog_nonparametric_predict=None):
"""Computes fitted values and marginal effects"""
if exog_predict is None:
exog_predict = self.exog
else:
exog_predict = _adjust_shape(exog_predict, self.k_linear)
if exog_nonparametric_predict is None:
exog_nonparametric_predict = self.exog_nonparametric
else:
exog_nonparametric_predict = _adjust_shape(exog_nonparametric_predict, self.K)
N_data_predict = np.shape(exog_nonparametric_predict)[0]
mean = np.empty((N_data_predict,))
mfx = np.empty((N_data_predict, self.K))
Y = self.endog - np.dot(exog_predict, self.b)[:,None]
for i in xrange(N_data_predict):
mean_mfx = self.func(self.bw, Y, self.exog_nonparametric,
data_predict=exog_nonparametric_predict[i, :])
mean[i] = mean_mfx[0]
mfx_c = np.squeeze(mean_mfx[1])
mfx[i, :] = mfx_c
return mean, mfx
def __init__(self, endog, exog, var_type):
self.var_type = var_type
self.K = len(var_type)
self.var_type = self.var_type[0]
self.endog = _adjust_shape(endog, 1)
self.exog = _adjust_shape(exog, self.K)
self.nobs = np.shape(self.exog)[0]
self.data_type = self.var_type
self.func = self._est_loc_linear
self.b, self.bw = self._est_b_bw()
def __init__(self, endog, exog, exog_nonparametric, var_type, k_linear):
self.endog = _adjust_shape(endog, 1)
self.exog = _adjust_shape(exog, k_linear)
self.K = len(var_type)
self.exog_nonparametric = _adjust_shape(exog_nonparametric, self.K)
self.k_linear = k_linear
self.nobs = np.shape(self.exog)[0]
self.var_type = var_type
self.data_type = self.var_type
self.func = self._est_loc_linear
self.b, self.bw = self._est_b_bw()
def __init__(self, endog, exog, var_type):
self.var_type = var_type
self.K = len(var_type)
self.var_type = self.var_type[0]
self.endog = _adjust_shape(endog, 1)
self.exog = _adjust_shape(exog, self.K)
self.nobs = np.shape(self.exog)[0]
self.data_type = self.var_type
self.ckertype = 'gaussian'
self.okertype = 'wangryzin'
self.ukertype = 'aitchisonaitken'
self.func = self._est_loc_linear
self.b, self.bw = self._est_b_bw()
def fit(self, data_predict=None):
if data_predict is None:
data_predict = self.exog
else:
data_predict = _adjust_shape(data_predict, self.K)
N_data_predict = np.shape(data_predict)[0]
mean = np.empty((N_data_predict,))
mfx = np.empty((N_data_predict, self.K))
for i in xrange(N_data_predict):
mean_mfx = self.func(self.bw, self.endog,
np.dot(self.exog, self.b)[:,None],
data_predict=np.dot(data_predict[i:i+1, :],self.b))
mean[i] = mean_mfx[0]
mfx_c = np.squeeze(mean_mfx[1])
mfx[i, :] = mfx_c
return mean, mfx
