def Bootstrapwrapper(self, chi):
'''Wrapper method for ADF Chi2 bootstrap'''
# Run boostrap
s, gamma = self.BootstrapChiADF()
s = pd.DataFrame(s, index = self.mod_semopy.vars['observed'],\
columns = self.mod_semopy.vars['observed'])
# Get new results (no need to run inspector)
# NOTE: in May 2021 semopy cannot handle custom weighting matrices for
# its WLS procedure. Make sure you mod semopy such that it can handle them
model = Model(self.equation,
mimic_lavaan=self.mimic_lavaan,
baseline=self.bool_baseline)
est = model.fit(self.data,
cov=s,
obj=self.obj,
solver=self.solver,
custom_w=gamma)
# Get Chi2_b, Corrected Chi2 and the chi2 bias
chi_b = stats.calc_chi2(model)[0]
chi_corr = 2 * chi - chi_b
bias_chi2 = chi_b - chi
return chi_b, chi_corr, bias_chi2
def sem(name, data):
desc = '''
# measurement model
# *** DOMAINS ***
employment =~ number_of_jobs + income_from_jobs
# + sex
housing =~ housing_space + sanitation_of_housing
# + relocation_to_bhasanchar
education =~ quality_of_education + lt_12_education + between_12_18_education + gt_18_education
health =~ psychological_healthcare + number_of_healthcare_facilities + quality_of_healthcare
social_links =~ help_from_ngo + trust_on_ngo + trust_on_law_enforcement
social_bridges =~ possibility_of_friendship_with_host + friendship_with_host + marriage_to_host
social_bonds =~ bond_with_rohingyas_outside + bond_with_neighbors + bond_with_majhis
# + number_of_friends + bond_with_imams
language_and_cultural_knowledge =~ number_of_religious_facilities + removal_of_religious_barriers + cultural_mixability
safety_and_stability =~ intention_to_leave + improvement_in_6mos + feeling_about_future + number_of_violence + fear_of_children_safety
# + stress + knowledge_of_missing_children + number_of_sexual_harassment + fear_of_leaving_again + discussion_about_violent_groups
rights_and_citizenship =~ repatriation_in_home + return_to_home + rights_in_home
# *** THEMES ***
markers_and_means =~ education + employment + housing + health
social_connections =~ social_bonds + social_bridges + social_links
facilitators =~ language_and_cultural_knowledge + safety_and_stability
foundation =~ rights_and_citizenship
integration =~ markers_and_means + social_connections + facilitators + foundation
# regressions
number_of_jobs ~ sex # should we use it?
# residual correlations
removal_of_religious_barriers ~~ number_of_religious_facilities
'''
mod = Model(desc)
res = mod.fit(data)
ins = mod.inspect()
# pprint(ins)
ins.to_excel('../result/inspect_' + name + '.xlsx', index=False)
stat = semopy.calc_stats(mod)
stat = stat.T
stat.to_excel('../result/stat_' + name + '.xlsx')
g = semopy.semplot(mod, '../plots/' + name + '.pdf')
def semopyFit(self, dataframe):
''''Fits the model with semopy. Helper function. '''
# Set model
model = Model(self.equation,
mimic_lavaan=self.mimic_lavaan,
baseline=self.bool_baseline)
# Estimate model
est = model.fit(dataframe, obj=self.obj, solver=self.solver)
# Get results
res = model.inspect(std_est=True, se_robust=self.bool_robust)
return model, est, res
import semopy from semopy import Model import pandas as pd from pprint import pprint desc = semopy.examples.political_democracy.get_model() print(desc) data = semopy.examples.political_democracy.get_data() # print(type(data), data.columns) mod = Model(desc) res = mod.fit(data) # print(res) ins = mod.inspect() # pprint(ins)
logliks = np.zeros((n_subjects, n_models)) # likelihoods
lmes = np.zeros((n_subjects, n_models)) # LMEs = -0.5 * BICs
bics = np.zeros((n_subjects, n_models)) # BICs
ks = np.zeros((n_subjects, n_models)) # # params
ns = np.zeros((n_subjects, n_models)) # # data points
for i in range(n_subjects):
for j in range(n_models):
model = Model(descs[j])
filepath = os.path.join(dirname, files[i])
data = pd.read_csv(filepath, sep='\t')
opt_res = model.fit(data)
# from /Users/momchil/anaconda3/lib/python3.7/site-packages/semopy/stats.py: calc_bic()
# WARNING: all of these are up to a proportionality constant that DIFFERS ACROSS SUBJECTS => do not use for BMS
#
'''
logliks[i,j] = stats.calc_likelihood(model) # note up to proportionality constant, b/c w.r.t. saturated model, but that's the same for all models so it's fine
ks[i,j], ns[i,j] = len(model.param_vals), model.mx_data.shape[0]
bic = stats.calc_bic(model)
lmes[i,j] = -0.5 * bic
'''
# calculate likelihood manually, following https://en.wikipedia.org/wiki/Wishart_distribution
# and https://math.stackexchange.com/questions/2803164/degrees-of-freedom-in-a-wishart-distribution
# and /Users/momchil/anaconda3/lib/python3.7/site-packages/semopy/stats.py: obj_mlw()
# and /Users/momchil/anaconda3/lib/python3.7/site-packages/semopy/stats.py: calc_bic()