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cleaned up some overly long lines
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@msrosenberg
msrosenberg committed Aug 22, 2024
1 parent 0698fe8 commit c7baf4d
Showing 1 changed file with 20 additions and 11 deletions.
31 changes: 20 additions & 11 deletions src/MetaWinAnalysisFunctions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -650,7 +650,8 @@ def simple_meta_analysis(data, options, decimal_places: int = 4, alpha: float =
forest_data = [mean_data]
for i in range(n):
# individual study data has to use normal dist
tmp_lower, tmp_upper = scipy.stats.norm.interval(confidence=1-alpha, loc=e_data[i], scale=math.sqrt(v_data[i]))
tmp_lower, tmp_upper = scipy.stats.norm.interval(confidence=1-alpha, loc=e_data[i],
scale=math.sqrt(v_data[i]))
study_data = mean_data_tuple(study_names[i], plot_order, 0, e_data[i], None, 0, 0, tmp_lower, tmp_upper,
None, None, None, None)
forest_data.append(study_data)
Expand Down Expand Up @@ -822,7 +823,8 @@ def grouped_meta_analysis(data, options, decimal_places: int = 4, alpha: float =
if norm_ci:
lower_ci, upper_ci = scipy.stats.norm.interval(confidence=1 - alpha, loc=mean_e, scale=math.sqrt(var_e))
else:
lower_ci, upper_ci = scipy.stats.t.interval(confidence=1 - alpha, df=n-1, loc=mean_e, scale=math.sqrt(var_e))
lower_ci, upper_ci = scipy.stats.t.interval(confidence=1 - alpha, df=n-1, loc=mean_e,
scale=math.sqrt(var_e))
lower_bs_ci, upper_bs_ci, lower_bias_ci, upper_bias_ci = bootstrap_means(options.bootstrap_mean, boot_data,
mean_e, pooled_var,
options.random_effects, alpha,
Expand Down Expand Up @@ -990,7 +992,8 @@ def cumulative_meta_analysis(data, options, decimal_places: int = 4, alpha: floa
if norm_ci:
lower_ci, upper_ci = scipy.stats.norm.interval(confidence=1-alpha, loc=mean_e, scale=math.sqrt(var_e))
else:
lower_ci, upper_ci = scipy.stats.t.interval(confidence=1-alpha, df=df, loc=mean_e, scale=math.sqrt(var_e))
lower_ci, upper_ci = scipy.stats.t.interval(confidence=1-alpha, df=df, loc=mean_e,
scale=math.sqrt(var_e))
lower_bs_ci, upper_bs_ci, lower_bias_ci, upper_bias_ci = bootstrap_means(options.bootstrap_mean, tmp_boot,
mean_e, pooled_var,
options.random_effects, alpha,
Expand Down Expand Up @@ -1117,7 +1120,8 @@ def regression_meta_analysis(data, options, decimal_places: int = 4, alpha: floa
if norm_ci:
lower_ci, upper_ci = scipy.stats.norm.interval(confidence=1 - alpha, loc=mean_e, scale=math.sqrt(var_e))
else:
lower_ci, upper_ci = scipy.stats.t.interval(confidence=1 - alpha, df=n-1, loc=mean_e, scale=math.sqrt(var_e))
lower_ci, upper_ci = scipy.stats.t.interval(confidence=1 - alpha, df=n-1, loc=mean_e,
scale=math.sqrt(var_e))
lower_bs_ci, upper_bs_ci, lower_bias_ci, upper_bias_ci = bootstrap_means(options.bootstrap_mean, boot_data,
mean_e, pooled_var,
options.random_effects, alpha,
Expand Down Expand Up @@ -1387,7 +1391,8 @@ def complex_meta_analysis(data, options, decimal_places: int = 4, alpha: float =
if norm_ci:
lower_ci, upper_ci = scipy.stats.norm.interval(confidence=1 - alpha, loc=mean_e, scale=math.sqrt(var_e))
else:
lower_ci, upper_ci = scipy.stats.t.interval(confidence=1 - alpha, df=df, loc=mean_e, scale=math.sqrt(var_e))
lower_ci, upper_ci = scipy.stats.t.interval(confidence=1 - alpha, df=df, loc=mean_e,
scale=math.sqrt(var_e))
lower_bs_ci, upper_bs_ci, lower_bias_ci, upper_bias_ci = bootstrap_means(options.bootstrap_mean, boot_data,
mean_e, pooled_var,
options.random_effects, alpha,
Expand Down Expand Up @@ -1431,7 +1436,8 @@ def complex_meta_analysis(data, options, decimal_places: int = 4, alpha: float =
for b in range(len(beta)):
se = math.sqrt(sigma_b[b, b])
p = prob_z_score(beta[b]/se)
predictor_table_data.append(predictor_test_tuple("β{} ({})".format(b, predictor_labels[b]), beta[b], se, p))
predictor_table_data.append(predictor_test_tuple("β{} ({})".format(b, predictor_labels[b]),
beta[b], se, p))
output_blocks.append(predictor_table(predictor_table_data, decimal_places))

global_het_data = heterogeneity_test_tuple(get_text("Total"), qt, df, pqt, "")
Expand Down Expand Up @@ -1699,7 +1705,8 @@ def nested_meta_analysis(data, options, decimal_places: int = 4, alpha: float =
if norm_ci:
lower_ci, upper_ci = scipy.stats.norm.interval(confidence=1 - alpha, loc=mean_e, scale=math.sqrt(var_e))
else:
lower_ci, upper_ci = scipy.stats.t.interval(confidence=1 - alpha, df=n-1, loc=mean_e, scale=math.sqrt(var_e))
lower_ci, upper_ci = scipy.stats.t.interval(confidence=1 - alpha, df=n-1, loc=mean_e,
scale=math.sqrt(var_e))
lower_bs_ci, upper_bs_ci, lower_bias_ci, upper_bias_ci = bootstrap_means(options.bootstrap_mean, boot_data,
mean_e, 0, False, alpha,
progress_bar=progress_bar)
Expand Down Expand Up @@ -2207,7 +2214,8 @@ def phylogenetic_meta_analysis(data, options, tree, decimal_places: int = 4, alp
for b in range(len(beta)):
se = math.sqrt(sigma_b[b, b])
p = prob_z_score(beta[b]/se)
predictor_table_data.append(predictor_test_tuple("β{} ({})".format(b, predictor_labels[b]), beta[b], se, p))
predictor_table_data.append(predictor_test_tuple("β{} ({})".format(b, predictor_labels[b]),
beta[b], se, p))
output_blocks.append(predictor_table(predictor_table_data, decimal_places))

global_het_data = heterogeneity_test_tuple(get_text("Total"), qt, df, pqt, "")
Expand All @@ -2223,16 +2231,17 @@ def phylogenetic_meta_analysis(data, options, tree, decimal_places: int = 4, alp
mean_e = beta[0]
var_e = sigma_b[0, 0]
mean_v = numpy.sum(v_data) / n
lower_ci, upper_ci = scipy.stats.t.interval(confidence=1 - alpha, df=df, loc=mean_e, scale=math.sqrt(var_e))
lower_ci, upper_ci = scipy.stats.t.interval(confidence=1 - alpha, df=df, loc=mean_e,
scale=math.sqrt(var_e))
mean_data = mean_data_tuple(get_text("Global"), 0, n, mean_e, None, var_e, mean_v, lower_ci, upper_ci,
0, 0, 0, 0)

i2, i2_lower, i2_upper = calc_i2(qt, n, alpha)
i2_data = [i2_values(get_text("Total"), i2, i2_lower, i2_upper)]

output_blocks.append(["<h3>{}</h3>".format(get_text("Global Results"))])
new_cites = create_global_output(output_blocks, effect_sizes.label, mean_data, global_het_data, pooled_var,
i2_data, options.bootstrap_mean, decimal_places, alpha,
new_cites = create_global_output(output_blocks, effect_sizes.label, mean_data, global_het_data,
pooled_var, i2_data, options.bootstrap_mean, decimal_places, alpha,
options.log_transformed, inc_median=False)
citations.extend(new_cites)
except numpy.linalg.LinAlgError as error_msg:
Expand Down

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