How to use the pymer4.utils._ols function in pymer4

else:
                dv = self.formula.split("~")[0]
                weight_groups = self.data.groupby(weights)
                weight_vals = 1 / weight_groups[dv].transform(np.var, ddof=1)
        else:
            weight_vals = weights
        if weights is None:
            self.estimator = "OLS"
        else:
            self.estimator = "WLS"

        y, x = dmatrices(self.formula, ddat, 1, return_type="dataframe")
        self.design_matrix = x

        # Compute standard estimates
        b, se, t, res = _ols(
            x,
            y,
            robust,
            all_stats=True,
            n_lags=n_lags,
            cluster=cluster,
            weights=weight_vals,
        )
        if cluster is not None:
            # Cluster corrected dof (num clusters - num coef)
            # Differs from stats and statsmodels which do num cluster - 1
            # Ref: http://cameron.econ.ucdavis.edu/research/Cameron_Miller_JHR_2015_February.pdf
            df = cluster.nunique() - x.shape[1]
        else:
            df = x.shape[0] - x.shape[1]
            if isinstance(weights, str) and wls_dof_correction:

"Model must be fit before partial correlations can be computed"
            )
        if corr_type not in ["semi", "partial"]:
            raise ValueError("corr_type must be 'semi' or 'partial'")
        from scipy.stats import pearsonr

        corrs = []
        corrs.append(np.nan)  # don't compute for intercept
        for c in self.design_matrix.columns[1:]:
            dv = self.formula.split("~")[0]
            other_preds = [e for e in self.design_matrix.columns[1:] if e != c]
            right_side = "+".join(other_preds)
            y, x = dmatrices(
                c + "~" + right_side, self.data, 1, return_type="dataframe"
            )
            pred_m_resid = _ols(
                x,
                y,
                robust=False,
                n_lags=1,
                cluster=None,
                all_stats=False,
                resid_only=True,
            )
            y, x = dmatrices(
                dv + "~" + right_side, self.data, 1, return_type="dataframe"
            )
            if corr_type == "semi":
                dv_m_resid = y.values.squeeze()
            elif corr_type == "partial":
                dv_m_resid = _ols(
                    x,

pred_m_resid = _ols(
                x,
                y,
                robust=False,
                n_lags=1,
                cluster=None,
                all_stats=False,
                resid_only=True,
            )
            y, x = dmatrices(
                dv + "~" + right_side, self.data, 1, return_type="dataframe"
            )
            if corr_type == "semi":
                dv_m_resid = y.values.squeeze()
            elif corr_type == "partial":
                dv_m_resid = _ols(
                    x,
                    y,
                    robust=False,
                    n_lags=1,
                    cluster=None,
                    all_stats=False,
                    resid_only=True,
                )
            corrs.append(pearsonr(dv_m_resid, pred_m_resid)[0])
        if ztrans_corrs:
            corrs = np.arctanh(corrs)
        return pd.Series(corrs, index=self.coefs.index)