def setup_class(cls):
data = ds.df
nobs = len(data)
data["dummy"] = (np.arange(nobs) < (nobs / 2)).astype(float)
# alias to correspond to patsy name
data["C(dummy)[T.1.0]"] = data["dummy"]
cls.data = data
columns = ['C(dummy)[T.1.0]', 'pared', 'public', 'gpa']
# standard fit
mod = OrderedModel(data['apply'].values.codes,
np.asarray(data[columns], float),
distr='logit')
cls.res = mod.fit(method='bfgs', disp=False)
# standard fit with pandas input
modp = OrderedModel(data['apply'],
data[columns],
distr='logit')
cls.resp = modp.fit(method='bfgs', disp=False)
def main():
###
### input paths
###
input_df_path = sys.argv[
1] # dataframe with the combined questionnaire data
input_question_overview = sys.argv[
2] # datafrane with the question ids over time
input_pgs_path = sys.argv[
3] # dataframe with the PGS values of the participants
output_dir_ori_path = sys.argv[4] # output directory path
suffix = sys.argv[5] # suffix fot the outputs
pgs_id = int(
sys.argv[7]
) # int of pgs number, used for multi node analysis on cluster
question_prs_selection_path = sys.argv[
6] # input path of question and prs selection
selected_trait_file_path = sys.argv[
7] # input path of trait selection file
model_selection_file = sys.argv[8] # input path of trait selection file
df_question_prs_selection = pd.read_csv(question_prs_selection_path,
sep="\t")
# create output directory
create_dir(output_dir_ori_path)
# create log file
log_file_path = "log_file_{id}_{suffix}_{date}.txt".format(
id=pgs_id, suffix=suffix, date=datetime.now().strftime("%d-%m-%Y"))
logfile = open(os.path.join(output_dir_ori_path, log_file_path), "w")
# read input files
df_quest = pd.read_pickle(input_df_path)
df_question_ids_total = pd.read_csv(input_question_overview,
sep="\t",
index_col=0,
dtype="str")
df_question_ids = df_question_ids_total.loc[:,
df_question_ids_total.columns.
difference([
"Number of timepoints",
"Question answers"
])]
df_question_ids.columns = df_question_ids.columns.astype(float)
df_question_ids = df_question_ids.T
df_question_ids = df_question_ids.sort_index()
trait_subset = pd.read_pickle(input_pgs_path)
# Filter questionnaire question data on meta information
start_columns = df_quest.columns[df_quest.columns.str.startswith("covt")]
date_columns = df_quest.columns[df_quest.columns.str.endswith("DATE")]
age_columns = df_quest.columns[df_quest.columns.str.endswith("AGE")]
gender_columns = df_quest.columns[df_quest.columns.str.endswith("GENDER")]
date_variant_id = df_quest.columns[df_quest.columns.str.endswith(
"VARIANT_ID")]
date_zip_code = df_quest.columns[df_quest.columns.str.endswith("ZIP_CODE")]
date_response_rate = df_quest.columns[df_quest.columns.str.contains(
"responsedate")]
skip_columns = [
"covt17_COVID172TXT", "covt17_COVID177TXT", "covt17_COVID192A",
"covt17_COVID192B", "covt17_PSEUDOIDEXT"
]
selection_columns = start_columns.difference(date_columns).difference(
age_columns).difference(gender_columns).difference(
date_variant_id).difference(date_zip_code).difference(
date_response_rate).difference(skip_columns)
df_quest = df_quest.loc[df_quest.index.intersection(trait_subset.index), :]
# trait selection
trait_subset.columns = trait_subset.columns.str.replace(
"/", ".").str.replace(" ", ".").str.replace("-", ".").str.replace(
"(", ".").str.replace(")", ".")
df_selected_traits = pd.read_csv(selected_trait_file_path, header=None)
trait_subset = trait_subset.loc[:,
trait_subset.columns.
intersection(df_selected_traits.iloc[:,
0])]
trait_subset = trait_subset.iloc[:, [pgs_id]]
print("Process trade: {}".format(trait_subset.columns[0]))
# model covariate columns
correction_columns = [
"age_recent", "age2_recent", "chronic_recent", "household_recent",
"have_childs_at_home_recent", "gender_recent"
]
correction_df = df_quest.loc[:, correction_columns]
# question selection
df_selected_questions = pd.read_csv(model_selection_file,
sep="\t",
index_col="Question")
df_selected_questions = df_selected_questions.dropna(subset=["Type"])
question_list = df_selected_questions.index.intersection(
df_question_ids.columns)
# create output dataframes
multiIndex_columns = pd.MultiIndex.from_product(
[question_list, df_question_ids.index], names=["question", "quest_nr"])
df_betas_per_question = pd.DataFrame(index=trait_subset.columns,
columns=multiIndex_columns)
df_pvalues_per_question = pd.DataFrame(index=trait_subset.columns,
columns=multiIndex_columns)
df_se_values_per_question = pd.DataFrame(index=trait_subset.columns,
columns=multiIndex_columns)
n_values_per_question = []
value_counts_per_question = []
# process all questions
for column in question_list:
question_ids_of_question = df_question_ids.loc[:, column]
question_ids_of_question = question_ids_of_question[
~question_ids_of_question.isna()]
# create temp lists for model outputs
betas_per_week = []
pvalues_per_week = []
se_values_per_week = []
n_values_per_week = {}
values_counts_per_week = {}
# process all datapoints of the question
for index, quest_id in question_ids_of_question.iteritems():
if quest_id in selection_columns:
# create model input data
df_subset = df_quest.loc[:, quest_id]
df_subset = df_subset.astype(float)
df_subset = df_subset.dropna()
# find the correct model
model_type = df_selected_questions.loc[column, "Type"]
if model_type == "ordinal":
df_subset = df_subset.sort_index()
df_subset = df_subset.astype(int).astype("category")
model_type = "ordinal"
elif model_type == "ordinal-ordered" or model_type == "ordinal-ordered-turned":
df_subset = df_subset.sort_index()
df_subset = df_subset.astype(int).astype(
"category").cat.as_ordered()
model_type = "ordinal"
# fit all models per trait
beta_values_per_model = {}
pvalues_per_model = {}
se_values_per_model = {}
for pgs_column_name in trait_subset.columns:
if ((df_question_prs_selection["Question"] == column) &
(df_question_prs_selection["prs"]
== pgs_column_name)).any():
#write logfile info
logfile.write(
"\n\nProcess: {question}, {question_id}, {pgs}, {model}\n"
.format(question=column,
question_id=quest_id,
pgs=pgs_column_name,
model=model_type))
print("process", column, pgs_column_name)
try:
# create model and set the model parameters
df_model_input = pd.merge(
trait_subset.loc[:, [pgs_column_name]],
correction_df,
left_index=True,
right_index=True)
fit_args = {}
if model_type == "binomial":
df_model_input["intercept"] = 1.0
mod = sm.Logit(
df_subset,
df_model_input.loc[df_subset.index, :])
fit_args["maxiter"] = 10000
elif model_type == "ordinal":
mod = OrderedModel(
df_subset,
df_model_input.loc[df_subset.index, :],
distr="logit")
fit_args["method"] = 'bfgs'
fit_args["maxiter"] = 10000
else:
df_model_input["intercept"] = 1.0
mod = sm.OLS(
df_subset,
df_model_input.loc[df_subset.index, :])
# fit the model
res = mod.fit(**fit_args)
#write model output
print(res.summary())
logfile.write(res.summary().as_text())
logfile.write("\n")
#save output from the model
beta_values_per_model[
pgs_column_name] = res.params[pgs_column_name]
pvalues_per_model[pgs_column_name] = res.pvalues[
pgs_column_name]
se_values_per_model[pgs_column_name] = res.bse[
pgs_column_name]
except sm.tools.sm_exceptions.PerfectSeparationError:
print("Exception PerfectSeparationError",
pgs_column_name, quest_id, column)
logfile.write(
"Error PerfectSeparationError: {question_id}, {pgs}\n"
.format(
question_id=quest_id,
pgs=pgs_column_name,
))
continue
except np.linalg.LinAlgError:
print("Exception LinAlgError", pgs_column_name,
quest_id, column)
logfile.write(
"Error LinAlgError: {question_id}, {pgs}\n".
format(
question_id=quest_id,
pgs=pgs_column_name,
))
continue
except UnboundLocalError:
print("Exception UnboundLocalError",
pgs_column_name, quest_id, column)
logfile.write(
"Error UnboundLocalError: {question_id}, {pgs}\n"
.format(
question_id=quest_id,
pgs=pgs_column_name,
))
continue
except Exception:
print("Exception", pgs_column_name, quest_id,
column)
logfile.write(
"Error Exception (general): {question_id}, {pgs}\n"
.format(
question_id=quest_id,
pgs=pgs_column_name,
))
continue
# save all the model output per time point
model_betas = pd.Series(beta_values_per_model, name=index)
betas_per_week.append(model_betas)
model_pvalues = pd.Series(pvalues_per_model, name=index)
pvalues_per_week.append(model_pvalues)
model_se_values = pd.Series(se_values_per_model, name=index)
se_values_per_week.append(model_se_values)
n_values_per_week[index] = df_subset.shape[0]
val_counts = df_subset.value_counts()
val_counts.index = val_counts.index.astype(int).astype(str)
values_counts_per_week[index] = json.dumps(
val_counts.to_dict())
# save all the model information per PRS, per time point, per question (multi column table)
if len(betas_per_week) > 0:
df_model_betas = pd.concat(betas_per_week, axis=1)
df_model_betas.columns = pd.MultiIndex.from_product(
[[column], df_model_betas.columns])
df_betas_per_question.loc[df_model_betas.index,
df_model_betas.columns] = df_model_betas
df_model_pvalues = pd.concat(pvalues_per_week, axis=1)
df_model_pvalues.columns = pd.MultiIndex.from_product(
[[column], df_model_pvalues.columns])
df_pvalues_per_question.loc[
df_model_pvalues.index,
df_model_pvalues.columns] = df_model_pvalues
df_model_se_values = pd.concat(se_values_per_week, axis=1)
df_model_se_values.columns = pd.MultiIndex.from_product(
[[column], df_model_se_values.columns])
df_se_values_per_question.loc[
df_model_se_values.index,
df_model_se_values.columns] = df_model_se_values
n_values_per_question.append(
pd.Series(n_values_per_week, name=column))
value_counts_per_question.append(
pd.Series(values_counts_per_week, name=column))
df_nvalues = pd.concat(n_values_per_question, axis=1)
df_value_counts = pd.concat(value_counts_per_question, axis=1)
logfile.close()
print("correlations calculation ready")
# save the results per PRS in separated folders
for PRS_name in df_betas_per_question.index:
print("PRS_name", PRS_name)
#get the export data per PRS from the dataframes
df_prs_subset = df_betas_per_question.loc[PRS_name, :]
df_prs_subset = df_prs_subset.unstack(level=-1)
df_prs_subset_pvalues = df_pvalues_per_question.loc[PRS_name, :]
df_prs_subset_pvalues = df_prs_subset_pvalues.unstack(level=-1)
df_prs_subset_se_values = df_se_values_per_question.loc[PRS_name, :]
df_prs_subset_se_values = df_prs_subset_se_values.unstack(level=-1)
time_series_info_corr = df_prs_subset.copy()
time_series_info_corr.columns = time_series_info_corr.columns.astype(
str)
# create the PRS output dir
output_prs_dir = os.path.join(output_dir_ori_path, PRS_name)
create_dir(output_prs_dir)
# export the files
df_prs_subset_pvalues.columns = df_prs_subset_pvalues.columns.astype(
str)
export_df(df_prs_subset_pvalues, output_prs_dir, PRS_name,
"p_values_{}".format(suffix))
df_prs_subset_se_values.columns = df_prs_subset_se_values.columns.astype(
str)
export_df(df_prs_subset_se_values, output_prs_dir, PRS_name,
"se_values_{}".format(suffix))
df_nvalues = df_nvalues.T
print(df_nvalues)
df_nvalues.columns = df_nvalues.columns.astype(str)
export_df(df_nvalues, output_prs_dir, PRS_name,
"n_values_{}".format(suffix))
df_value_counts = df_value_counts.T
df_value_counts.columns = df_value_counts.columns.astype(str)
export_df(df_value_counts, output_prs_dir, PRS_name,
"value_counts_{}".format(suffix))
time_series_info_corr["Question answers"] = df_selected_questions.loc[
time_series_info_corr.index, "Question answers"]
time_series_info_corr = time_series_info_corr.loc[:, [
"Question answers", *list(df_prs_subset.columns.astype(str))
]]
export_df(time_series_info_corr, output_prs_dir, PRS_name,
"correlations_{}".format(suffix))
def ordinal_regression_formula(data, formula, distr="probit"):
model = OrderedModel.from_formula(formula=formula, data=data, distr=distr)
result = model.fit(method="bfgs")
summary = result.summary()
odds_radio = get_odds_radio(result)
return result, summary, odds_radio
def ordinal_regression(x, y, distr="probit"):
model = OrderedModel(y, x, distr=distr)
result = model.fit(method="bfgs")
summary = result.summary()
odds_radio = get_odds_radio(result)
return result, summary, odds_radio
data = pd.read_csv(r"D:/书籍资料整理/属性数据分析/政治意识与党派.csv")
# data['意识形态']=data['意识形态'].replace({'很自由':1,'有点自由':2,'中等':3,'有点保守':4,'很保守':5})
data['政治党派'] = data['政治党派'].replace({'民主党人': 1, '共和党人': 0})
tmp = pd.DataFrame()
for i in range(0, 20):
tmp = tmp.append([data.loc[i]] * data.iloc[i]['值'])
tmp = tmp.reset_index()
del tmp['值']
del tmp['index']
# tmp.to_csv(r'D:/书籍资料整理/属性数据分析/政治意识与党派_整理数据.csv')
#得到的结果显示,自变量参数是反的.这个可以解释,因为使用的是α-βx展示
#书中的结果是α+βx
#但是截距从第二个开始就相去甚远很难找到解释理由,OrderedModel这个功能
#并非包内本身带的,文档也几乎没有提到.
#这个是将要被statsmodels带入的功能并没有完善待后续.
tmp['意识形态'] = tmp['意识形态'].astype('category')
s = pd.Series(["a", "b", "c", "a", "d", "e"])
cat_type = CategoricalDtype(categories=['很自由', '有点自由', '中等', '有点保守', '很保守'],
ordered=True) #categories必须是一个列表
tmp['意识形态'] = tmp['意识形态'].astype(cat_type)
modf_logit = OrderedModel.from_formula("意识形态~政治党派", tmp, distr='logit')
resf_logit = modf_logit.fit(method='bfgs')
print(resf_logit.summary())
data = pd.read_csv(r"D:/书籍资料整理/属性数据分析/心灵伤害与SES.csv")
data['心理伤害'] = data['心理伤害'].replace({'健康': 0, '轻度': 1, '中等': 2, '受损': 3})
modf_logit = OrderedModel.from_formula("心理伤害~SES+生活事件", data, distr='logit')
resf_logit = modf_logit.fit()
resf_logit.summary()
import pandas
from statsmodels.miscmodels.ordinal_model import OrderedModel
nobs, k_vars = 1000, 3
x = np.random.randn(nobs, k_vars)
# x = np.column_stack((np.ones(nobs), x))
# #constant will be in integration limits
xb = x.dot(np.ones(k_vars))
y_latent = xb + np.random.randn(nobs)
y = np.round(np.clip(y_latent, -2.4, 2.4)).astype(int) + 2
print(np.unique(y))
print(np.bincount(y))
mod = OrderedModel(y, x)
# start_params = np.ones(k_vars + 4)
# start_params = np.concatenate((np.ones(k_vars), np.arange(4)))
start_ppf = stats.norm.ppf((np.bincount(y) / len(y)).cumsum())
start_threshold = np.concatenate(
(start_ppf[:1], np.log(np.diff(start_ppf[:-1]))))
start_params = np.concatenate((np.zeros(k_vars), start_threshold))
res = mod.fit(start_params=start_params, maxiter=5000, maxfun=5000)
print(res.params)
# res = mod.fit(start_params=res.params, method='bfgs')
res = mod.fit(start_params=start_params, method='bfgs')
print(res.params)
print(np.exp(res.params[-(mod.k_levels - 1):]).cumsum())
# print(res.summary())
# categorical type, this is preferred over NumPy arrays.
# The model is based on a numerical latent variable $y_{latent}$ that we
# cannot observe but that we can compute thanks to exogenous variables.
# Moreover we can use this $y_{latent}$ to define $y$ that we can observe.
#
# For more details see the the Documentation of OrderedModel, [the UCLA
# webpage](https://stats.idre.ucla.edu/r/dae/ordinal-logistic-regression/)
# or this
# [book](https://onlinelibrary.wiley.com/doi/book/10.1002/9780470594001).
#
# ### Probit ordinal regression:
mod_prob = OrderedModel(data_student['apply'],
data_student[['pared', 'public', 'gpa']],
distr='probit')
res_prob = mod_prob.fit(method='bfgs')
res_prob.summary()
# In our model, we have 3 exogenous variables(the $\beta$s if we keep the
# documentation's notations) so we have 3 coefficients that need to be
# estimated.
#
# Those 3 estimations and their standard errors can be retrieved in the
# summary table.
#
# Since there are 3 categories in the target variable(`unlikely`,
# `somewhat likely`, `very likely`), we have two thresholds to estimate.
# As explained in the doc of the method