def test_bootstrapped_ma(self):
''' bootstrapped meta analysis '''
studies = self.model.get_studies_in_current_order()
method = "continuous.random"
current_param_vals = {'conf.level': 95.0, 'histogram.xlab': 'Effect Size', 'fp_xticks': '[default]', 'fp_show_col4': False, 'fp_show_col3': False, 'fp_xlabel': u'[default]', 'fp_show_col1': True, 'fp_show_col2': True, 'fp_outpath': u'./r_tmp/forest.png', 'rm.method': 'DL', 'fp_plot_ub': '[default]', 'fp_col1_str': u'Studies', 'measure': 'SMD', 'fp_plot_lb': '[default]', 'bootstrap.plot.path': '/Users/george/git/OpenMEE/r_tmp/bootstrap.png', 'digits': 3, 'fp_col2_str': u'[default]', 'num.bootstrap.replicates': 1000, 'fp_col4_str': u'Ev/Ctrl', 'histogram.title': 'Bootstrap Histogram', 'fp_col3_str': u'Ev/Trt', 'fp_show_summary_line': True, 'bootstrap.type': 'boot.ma'}
data_location = {'experimental_mean': 2, 'effect_size': 18, 'experimental_std_dev': 5, 'experimental_sample_size': 1, 'control_std_dev': 5, 'control_sample_size': 1, 'variance': 19, 'control_mean': 4}
meta_f_str = "bootstrap.continuous"
python_to_R.dataset_to_simple_continuous_robj(model=self.model,
included_studies=studies,
data_location=data_location,
data_type=CONTINUOUS,
covs_to_include=[])
result = python_to_R.run_meta_method(meta_function_name = meta_f_str,
function_name = method,
params = current_param_vals)
# assert result is not None
self.assertIsNotNone(result, "Result is unexpectedly none!")
def test_loo(self): # leave one out
''' leave_one_out meta analysis '''
studies = self.model.get_studies_in_current_order()
method = "continuous.random"
current_param_vals = {'conf.level': 95.0, 'digits': 3, 'fp_col2_str': u'[default]', 'fp_show_col4': False, 'fp_xlabel': u'[default]', 'fp_col4_str': u'Ev/Ctrl', 'fp_xticks': '[default]', 'fp_col3_str': u'Ev/Trt', 'fp_show_col3': False, 'fp_show_col2': True, 'fp_show_col1': True, 'fp_plot_lb': '[default]', 'fp_outpath': u'./r_tmp/forest.png', 'rm.method': 'DL', 'fp_plot_ub': '[default]', 'fp_col1_str': u'Studies', 'measure': 'SMD', 'fp_show_summary_line': True}
data_location = {'experimental_mean': 2, 'effect_size': 18, 'experimental_std_dev': 5, 'experimental_sample_size': 1, 'control_std_dev': 5, 'control_sample_size': 1, 'variance': 19, 'control_mean': 4}
meta_f_str = "loo.ma.continuous"
python_to_R.dataset_to_simple_continuous_robj(model=self.model,
included_studies=studies,
data_location=data_location,
data_type=CONTINUOUS,
covs_to_include=[])
result = python_to_R.run_meta_method(meta_function_name = meta_f_str,
function_name = method,
params = current_param_vals)
# assert result is not None
self.assertIsNotNone(result, "Result is unexpectedly none!")
