Python OLS.t_test方法代码示例

# 需要导入模块: from statsmodels.regression.linear_model import OLS [as 别名]
# 或者: from statsmodels.regression.linear_model.OLS import t_test [as 别名]
 def setupClass(cls):
     R = np.zeros(7)
     R[4:6] = [1,-1]
     data = longley.load()
     data.exog = add_constant(data.exog, prepend=False)
     res1 = OLS(data.endog, data.exog).fit()
     cls.Ttest1 = res1.t_test(R)

# 需要导入模块: from statsmodels.regression.linear_model import OLS [as 别名]
# 或者: from statsmodels.regression.linear_model.OLS import t_test [as 别名]
    def setupClass(cls):
#        if skipR:
#            raise SkipTest, "Rpy not installed"
#        try:
#            r.library('car')
#        except RPyRException:
#            raise SkipTest, "car library not installed for R"
        R = np.zeros(7)
        R[4:6] = [1,-1]
#        self.R = R
        data = longley.load()
        data.exog = add_constant(data.exog)
        res1 = OLS(data.endog, data.exog).fit()
        cls.Ttest1 = res1.t_test(R)

# 需要导入模块: from statsmodels.regression.linear_model import OLS [as 别名]
# 或者: from statsmodels.regression.linear_model.OLS import t_test [as 别名]
def get_tvalue_with_alternative_library(tested_vars, target_vars, covars=None):
    """Utility function to compute tvalues with linalg or statsmodels

    Massively univariate linear model (= each target is considered
    independently).

    Parameters
    ----------
    tested_vars: array-like, shape=(n_samples, n_regressors)
      Tested variates, the associated coefficient of which are to be tested
      independently with a t-test, resulting in as many t-values.

    target_vars: array-like, shape=(n_samples, n_targets)
      Target variates, to be approximated with a linear combination of
      the tested variates and the confounding variates.

    covars: array-like, shape=(n_samples, n_confounds)
      Confounding variates, to be fitted but not to be tested

    Returns
    -------
    t-values: np.ndarray, shape=(n_regressors, n_targets)

    """
    ### set up design
    n_samples, n_regressors = tested_vars.shape
    n_targets = target_vars.shape[1]
    if covars is not None:
        n_covars = covars.shape[1]
        design_matrix = np.hstack((tested_vars, covars))
    else:
        n_covars = 0
        design_matrix = tested_vars
    mask_covars = np.ones(n_regressors + n_covars, dtype=bool)
    mask_covars[:n_regressors] = False
    test_matrix = np.array([[1.] + [0.] * n_covars])

    ### t-values computation
    try:  # try with statsmodels if available (more concise)
        from statsmodels.regression.linear_model import OLS
        t_values = np.empty((n_targets, n_regressors))
        for i in range(n_targets):
            current_target = target_vars[:, i].reshape((-1, 1))
            for j in range(n_regressors):
                current_tested_mask = mask_covars.copy()
                current_tested_mask[j] = True
                current_design_matrix = design_matrix[:, current_tested_mask]
                ols_fit = OLS(current_target, current_design_matrix).fit()
                t_values[i, j] = np.ravel(ols_fit.t_test(test_matrix).tvalue)
    except:  # use linalg if statsmodels is not available
        from numpy import linalg
        lost_dof = n_covars + 1  # fit all tested variates independently
        t_values = np.empty((n_targets, n_regressors))
        for i in range(n_regressors):
            current_tested_mask = mask_covars.copy()
            current_tested_mask[i] = True
            current_design_matrix = design_matrix[:, current_tested_mask]
            invcov = linalg.pinv(current_design_matrix)
            normalized_cov = np.dot(invcov, invcov.T)
            t_val_denom_aux = np.diag(
                np.dot(test_matrix, np.dot(normalized_cov, test_matrix.T)))
            t_val_denom_aux = t_val_denom_aux.reshape((-1, 1))
            for j in range(n_targets):
                current_target = target_vars[:, j].reshape((-1, 1))
                res_lstsq = linalg.lstsq(current_design_matrix, current_target)
                residuals = (current_target
                             - np.dot(current_design_matrix, res_lstsq[0]))
                t_val_num = np.dot(test_matrix, res_lstsq[0])
                t_val_denom = np.sqrt(
                    np.sum(residuals ** 2, 0) / float(n_samples - lost_dof)
                    * t_val_denom_aux)
                t_values[j, i] = np.ravel(t_val_num / t_val_denom)
    return t_values