Ejemplo n.º 1
0
Archivo: ols.py Proyecto: zfs002/pandas
    def _f_stat_raw(self):
        """Returns the raw f-stat value."""
        from scipy.stats import f

        cols = self._x.columns

        if self._nw_lags is None:
            F = self._r2_raw / (self._r2_raw - self._r2_adj_raw)

            q = len(cols)
            if 'intercept' in cols:
                q -= 1

            shape = q, self.df_resid
            p_value = 1 - f.cdf(F, shape[0], shape[1])
            return F, shape, p_value

        k = len(cols)
        R = np.eye(k)
        r = np.zeros((k, 1))

        try:
            intercept = cols.get_loc('intercept')
            R = np.concatenate((R[0:intercept], R[intercept + 1:]))
            r = np.concatenate((r[0:intercept], r[intercept + 1:]))
        except KeyError:
            # no intercept
            pass

        return math.calc_F(R, r, self._beta_raw, self._var_beta_raw,
                           self._nobs, self.df)
Ejemplo n.º 2
0
    def _f_stat_raw(self):
        """Returns the raw f-stat value."""
        from scipy.stats import f

        cols = self._x.columns

        if self._nw_lags is None:
            F = self._r2_raw / (self._r2_raw - self._r2_adj_raw)

            q = len(cols)
            if 'intercept' in cols:
                q -= 1

            shape = q, self.df_resid
            p_value = 1 - f.cdf(F, shape[0], shape[1])
            return F, shape, p_value

        k = len(cols)
        R = np.eye(k)
        r = np.zeros((k, 1))

        try:
            intercept = cols.get_loc('intercept')
            R = np.concatenate((R[0: intercept], R[intercept + 1:]))
            r = np.concatenate((r[0: intercept], r[intercept + 1:]))
        except KeyError:
            # no intercept
            pass

        return math.calc_F(R, r, self._beta_raw, self._var_beta_raw,
                           self._nobs, self.df)
Ejemplo n.º 3
0
Archivo: ols.py Proyecto: zfs002/pandas
    def f_test(self, hypothesis):
        """Runs the F test, given a joint hypothesis.  The hypothesis is
        represented by a collection of equations, in the form

        A*x_1+B*x_2=C

        You must provide the coefficients even if they're 1.  No spaces.

        The equations can be passed as either a single string or a
        list of strings.

        Examples
        --------
        o = ols(...)
        o.f_test('1*x1+2*x2=0,1*x3=0')
        o.f_test(['1*x1+2*x2=0','1*x3=0'])
        """

        x_names = self._x.columns

        R = []
        r = []

        if isinstance(hypothesis, str):
            eqs = hypothesis.split(',')
        elif isinstance(hypothesis, list):
            eqs = hypothesis
        else:  # pragma: no cover
            raise Exception('hypothesis must be either string or list')
        for equation in eqs:
            row = np.zeros(len(x_names))
            lhs, rhs = equation.split('=')
            for s in lhs.split('+'):
                ss = s.split('*')
                coeff = float(ss[0])
                x_name = ss[1]

                if x_name not in x_names:
                    raise Exception('no coefficient named %s' % x_name)
                idx = x_names.get_loc(x_name)
                row[idx] = coeff
            rhs = float(rhs)

            R.append(row)
            r.append(rhs)

        R = np.array(R)
        q = len(r)
        r = np.array(r).reshape(q, 1)

        result = math.calc_F(R, r, self._beta_raw, self._var_beta_raw,
                             self._nobs, self.df)

        return f_stat_to_dict(result)
Ejemplo n.º 4
0
    def f_test(self, hypothesis):
        """Runs the F test, given a joint hypothesis.  The hypothesis is
        represented by a collection of equations, in the form

        A*x_1+B*x_2=C

        You must provide the coefficients even if they're 1.  No spaces.

        The equations can be passed as either a single string or a
        list of strings.

        Examples
        --------
        o = ols(...)
        o.f_test('1*x1+2*x2=0,1*x3=0')
        o.f_test(['1*x1+2*x2=0','1*x3=0'])
        """

        x_names = self._x.columns

        R = []
        r = []

        if isinstance(hypothesis, str):
            eqs = hypothesis.split(',')
        elif isinstance(hypothesis, list):
            eqs = hypothesis
        else:  # pragma: no cover
            raise Exception('hypothesis must be either string or list')
        for equation in eqs:
            row = np.zeros(len(x_names))
            lhs, rhs = equation.split('=')
            for s in lhs.split('+'):
                ss = s.split('*')
                coeff = float(ss[0])
                x_name = ss[1]

                if x_name not in x_names:
                    raise Exception('no coefficient named %s' % x_name)
                idx = x_names.get_loc(x_name)
                row[idx] = coeff
            rhs = float(rhs)

            R.append(row)
            r.append(rhs)

        R = np.array(R)
        q = len(r)
        r = np.array(r).reshape(q, 1)

        result = math.calc_F(R, r, self._beta_raw, self._var_beta_raw,
                             self._nobs, self.df)

        return f_stat_to_dict(result)
Ejemplo n.º 5
0
Archivo: ols.py Proyecto: zfs002/pandas
 def get_result(beta, vcov, n, d):
     return math.calc_F(R, r, beta, vcov, n, d)
Ejemplo n.º 6
0
 def get_result(beta, vcov, n, d):
     return math.calc_F(R, r, beta, vcov, n, d)