def test_smd(self):
# compare with metafor
yi = np.array([
0.09452415852032972, 0.27735586626551018, 0.36654442951591998,
0.66438496832691396, 0.46180628128769841, 0.18516443739910043
])
vi_asy = np.array([
0.03337056173559990, 0.03106510106366112, 0.05083971761755720,
0.01055175923267344, 0.04334466980873156, 0.02363025255552155
])
vi_ub = np.array([
0.03337176211751222, 0.03107388569950075, 0.05088098670518214,
0.01055698026322296, 0.04339077140867459, 0.02363252645927709
])
eff, var_eff = effectsize_smd(*self.dta)
# agreement with metafor is lower, atol for var 2.5e-06
# It's likely a small difference in bias correction
assert_allclose(eff, yi, rtol=1e-5)
assert_allclose(var_eff, vi_ub, rtol=1e-4)
assert_allclose(var_eff, vi_asy, rtol=2e-3) # not the same definition
# with unequal variance, not available yet
# > r = escalc(measure="SMDH", m1i=m.t, sd1i=sd.t, n1i=n.t, m2i=m.c,
# sd2i=sd.c, n2i=n.c, data=dat, vtype="UB")
yi = np.array([
0.09452415852032972, 0.27735586626551023, 0.36654442951591998,
0.66438496832691396, 0.46122883016705268, 0.18516443739910043
])
vi_ub = np.array([
0.03350541862210323, 0.03118164624093491, 0.05114625874744853,
0.01057160214284120, 0.04368303906568672, 0.02369839436451885
])
# compare with package `meta`
# high agreement, using smd function was written based on meta example
# > rm = metacont(n.t,m.t,sd.t,n.c,m.c,sd.c,
# + data=dat,studlab=rownames(dat),sm="SMD")
# > rm$TE
yi_m = np.array([
0.09452437336063831, 0.27735640148036095, 0.36654634845797818,
0.66438509989113559, 0.46180797677414176, 0.18516464424648887
])
# > rm$seTE**2
vi_m = np.array([
0.03337182573880991, 0.03107434965484927, 0.05088322525353587,
0.01055724834741877, 0.04339324466573324, 0.02363264537147130
])
assert_allclose(eff, yi_m, rtol=1e-13)
assert_allclose(var_eff, vi_m, rtol=1e-13)
]
colnames = ["study", "mean_t", "sd_t", "n_t", "mean_c", "sd_c", "n_c"]
rownames = [i[0] for i in data]
dframe1 = pd.DataFrame(data, columns=colnames)
rownames
mean2, sd2, nobs2, mean1, sd1, nobs1 = np.asarray(
dframe1[["mean_t", "sd_t", "n_t", "mean_c", "sd_c", "n_c"]]).T
rownames = dframe1["study"]
rownames.tolist()
np.array(nobs1 + nobs2)
# ### 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)