def test_dl(self):
res = results_meta.exk1_dl
eff, var_eff = self.eff, self.var_eff
tau2 = _fit_tau_mm(eff, var_eff, 1 / var_eff)
assert_allclose(tau2, res.tau2, atol=1e-10)
res3 = combine_effects(eff, var_eff, method_re="dl")
assert_allclose(res3.tau2, res.tau2, atol=1e-10)
assert_allclose(res3.mean_effect_re, res.b, atol=1e-13)
assert_allclose(res3.sd_eff_w_re, res.se, atol=1e-10)
ci = res3.conf_int(use_t=False) # fe, re, fe_wls, re_wls
assert_allclose(ci[1][0], res.ci_lb, atol=1e-10)
assert_allclose(ci[1][1], res.ci_ub, atol=1e-10)
assert_allclose(res3.q, res.QE, atol=1e-10)
# I2 is in percent in metafor
assert_allclose(res3.i2, res.I2 / 100, atol=1e-10)
assert_allclose(res3.h2, res.H2, atol=1e-10)
q, pv, df = res3.test_homogeneity()
assert_allclose(pv, res.QEp, atol=1e-10)
assert_allclose(q, res.QE, atol=1e-10)
assert_allclose(df, 9 - 1, atol=1e-10)
# compare FE estimate
res_fe = results_meta.exk1_fe
assert_allclose(res3.mean_effect_fe, res_fe.b, atol=1e-13)
assert_allclose(res3.sd_eff_w_fe, res_fe.se, atol=1e-10)
assert_allclose(ci[0][0], res_fe.ci_lb, atol=1e-10)
assert_allclose(ci[0][1], res_fe.ci_ub, atol=1e-10)
# compare FE, RE with HKSJ adjustment
res_dls = results_meta.exk1_dl_hksj
res_fes = results_meta.exk1_fe_hksj
assert_allclose(res3.mean_effect_re, res_dls.b, atol=1e-13)
assert_allclose(res3.mean_effect_fe, res_fes.b, atol=1e-13)
assert_allclose(res3.sd_eff_w_fe * np.sqrt(res3.scale_hksj_fe),
res_fes.se,
atol=1e-10)
assert_allclose(res3.sd_eff_w_re * np.sqrt(res3.scale_hksj_re),
res_dls.se,
atol=1e-10)
assert_allclose(np.sqrt(res3.var_hksj_fe), res_fes.se, atol=1e-10)
assert_allclose(np.sqrt(res3.var_hksj_re), res_dls.se, atol=1e-10)
# metafor uses t distribution for hksj
ci = res3.conf_int(use_t=True) # fe, re, fe_wls, re_wls
assert_allclose(ci[3][0], res_dls.ci_lb, atol=1e-10)
assert_allclose(ci[3][1], res_dls.ci_ub, atol=1e-10)
assert_allclose(ci[2][0], res_fes.ci_lb, atol=1e-10)
assert_allclose(ci[2][1], res_fes.ci_ub, atol=1e-10)
q, pv, df = res3.test_homogeneity()
assert_allclose(pv, res_dls.QEp, atol=1e-10)
assert_allclose(q, res_dls.QE, atol=1e-10)
assert_allclose(df, 9 - 1, atol=1e-10)
def setup_class(cls):
cls.res2 = res2 = results_meta.results_or_dl_hk
cls.dta = (res2.event_e, res2.n_e, res2.event_c, res2.n_c)
eff, var_eff = effectsize_2proportions(*cls.dta, statistic="or")
res1 = combine_effects(eff, var_eff, method_re="chi2", use_t=True)
cls.eff = eff
cls.var_eff = var_eff
cls.res1 = res1
def test_pm(self):
res = results_meta.exk1_metafor
eff, var_eff = self.eff, self.var_eff
tau2, converged = _fit_tau_iterative(eff,
var_eff,
tau2_start=0.1,
atol=1e-8)
assert_equal(converged, True)
assert_allclose(tau2, res.tau2, atol=1e-10)
# compare with WLS, PM weights
mod_wls = WLS(eff, np.ones(len(eff)), weights=1 / (var_eff + tau2))
res_wls = mod_wls.fit(cov_type="fixed_scale")
assert_allclose(res_wls.params, res.b, atol=1e-13)
assert_allclose(res_wls.bse, res.se, atol=1e-10)
ci_low, ci_upp = res_wls.conf_int()[0]
assert_allclose(ci_low, res.ci_lb, atol=1e-10)
assert_allclose(ci_upp, res.ci_ub, atol=1e-10)
# need stricter atol to match metafor,
# I also used higher precision in metafor
res3 = combine_effects(eff, var_eff, method_re="pm", atol=1e-7)
# TODO: asserts below are copy paste, DRY?
assert_allclose(res3.tau2, res.tau2, atol=1e-10)
assert_allclose(res3.mean_effect_re, res.b, atol=1e-13)
assert_allclose(res3.sd_eff_w_re, res.se, atol=1e-10)
ci = res3.conf_int(use_t=False)[1]
assert_allclose(ci[0], res.ci_lb, atol=1e-10)
assert_allclose(ci[1], res.ci_ub, atol=1e-10)
assert_allclose(res3.q, res.QE, atol=1e-10)
# the following does not pass yet
# assert_allclose(res3.i2, res.I2 / 100, atol=1e-10) # percent in R
# assert_allclose(res3.h2, res.H2, atol=1e-10)
th = res3.test_homogeneity()
q, pv = th
df = th.df
assert_allclose(pv, res.QEp, atol=1e-10)
assert_allclose(q, res.QE, atol=1e-10)
assert_allclose(df, 9 - 1, atol=1e-10)
# ### estimate effect size standardized mean difference
eff, var_eff = effectsize_smd(mean2, sd2, nobs2, mean1, sd1, nobs1)
# ### Using one-step chi2, DerSimonian-Laird estimate for random effects
# variance tau
#
# Method option for random effect `method_re="chi2"` or `method_re="dl"`,
# both names are accepted.
# This is commonly referred to as the DerSimonian-Laird method, it is
# based on a moment estimator based on pearson chi2 from the fixed effects
# estimate.
res3 = combine_effects(eff,
var_eff,
method_re="chi2",
use_t=True,
row_names=rownames)
# TODO: we still need better information about conf_int of individual
# samples
# We don't have enough information in the model for individual confidence
# intervals
# if those are not based on normal distribution.
res3.conf_int_samples(nobs=np.array(nobs1 + nobs2))
print(res3.summary_frame())
res3.cache_ci
res3.method_re
fig = res3.plot_forest()
