How to use the pymer4.models.Lm.Lm function in pymer4

self.group,
                    self.data[self.group].unique()[i],
                    self.ranked_data,
                )
                for i in range(self.data[self.group].nunique())
            )
            betas = np.array(betas)

        # Get the model matrix formula from patsy to make it more reliable to set the results dataframe index like Lmer
        y, x = dmatrices(self.formula, self.data, 1, return_type="dataframe")
        # Perform an intercept only regression for each beta
        results = []
        perm_ps = []
        for i in range(betas.shape[1]):
            df = pd.DataFrame({"X": np.ones_like(betas[:, i]), "Y": betas[:, i]})
            lm = Lm("Y ~ 1", data=df)
            lm.fit(
                robust=robust,
                conf_int=conf_int,
                summarize=False,
                n_boot=n_boot,
                n_jobs=n_jobs,
                n_lags=n_lags,
            )
            results.append(lm.coefs)
            if permute:
                # sign-flip permutation test for each beta instead to replace p-values
                if perm_on == 'coef':
                    return_stat = 'mean'
                else:
                    return_stat = 't-stat'
                seeds = np.random.randint(np.iinfo(np.int32).max, size=permute)