-- Fixture: small deterministic data with a text (categorical) predictor.
-- Kept literally in sync with scripts/parity_reference.R. R's conventions
-- apply: levels sorted ('F' < 'M' < 'Other'), first level is the reference
-- under treatment contrasts, so the terms are (Intercept), genderM,
-- genderOther.
CREATE TEMP TABLE t_factor (
    y      double precision,
    gender text
);
INSERT INTO t_factor VALUES
    (1.0, 'F'), (2.0, 'M'), (1.5, 'F'),
    (2.5, 'M'), (3.0, 'Other'), (2.8, 'Other');
SELECT term,
       round(estimate::numeric, 4)      AS estimate,
       round(std_error::numeric, 4)     AS std_error,
       round(statistic::numeric, 4)     AS statistic,
       round(p_value::numeric, 4)       AS p_value,
       round(conf_low_95::numeric, 4)   AS conf_low_95,
       round(conf_high_95::numeric, 4)  AS conf_high_95,
       family, link, formula,
       n_obs, n_used, n_dropped,
       round(aic::numeric, 4)           AS aic,
       round(deviance::numeric, 4)      AS deviance,
       round(null_deviance::numeric, 4) AS null_deviance
FROM fbsql.fit_glm(
    relation => $$ SELECT y, gender FROM t_factor $$,
    formula  => 'y ~ gender',
    family   => 'gaussian')
ORDER BY term COLLATE "C";  -- locale-independent row order for pg_regress
    term     | estimate | std_error | statistic | p_value | conf_low_95 | conf_high_95 |  family  |   link   |  formula   | n_obs | n_used | n_dropped |  aic   | deviance | null_deviance 
-------------+----------+-----------+-----------+---------+-------------+--------------+----------+----------+------------+-------+--------+-----------+--------+----------+---------------
 (Intercept) |   1.2500 |    0.2121 |    5.8926 |  0.0098 |      0.8342 |       1.6658 | gaussian | identity | y ~ gender |     6 |      6 |         0 | 6.4207 |   0.2700 |        3.0333
 genderM     |   1.0000 |    0.3000 |    3.3333 |  0.0446 |      0.4120 |       1.5880 | gaussian | identity | y ~ gender |     6 |      6 |         0 | 6.4207 |   0.2700 |        3.0333
 genderOther |   1.6500 |    0.3000 |    5.5000 |  0.0118 |      1.0620 |       2.2380 | gaussian | identity | y ~ gender |     6 |      6 |         0 | 6.4207 |   0.2700 |        3.0333
(3 rows)