junos
/
stress_at_work_analysis
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Correct adherence data to only count sessions once. 

Add age as a float predictor.
Obtain the same result with linear regression.
communication
junos 2021-06-08 22:32:14 +02:00
parent 1294ee40a0
commit 9cba88a6e3
1 changed files with 18 additions and 10 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

28
statistical_analysis/concordance.py
View File

@ -20,7 +20,7 @@ import datetime
import seaborn as sns
import pandas as pd
import statsmodels.api as sm
from statsmodels.formula.api import ols
import statsmodels.formula.api as smf
nb_dir = os.path.split(os.getcwd())[0]
if nb_dir not in sys.path:
@ -66,22 +66,30 @@ VARIABLES_TO_TRANSLATE = {
baseline_inactive.rename(columns=VARIABLES_TO_TRANSLATE, copy=False, inplace=True)
now = pd.Timestamp('now')
baseline_inactive = baseline_inactive.assign(date_of_birth = lambda x: pd.to_datetime(x.date_of_birth),
age = lambda x: now - x.date_of_birth)
age = lambda x: (now - x.date_of_birth).dt.days/365.25245)
# %%
df_session_counts
# %%
df_session_finished = df_session_counts[df_session_counts["session_response"] == "esm_finished"].reset_index()
# %%
df_participant_finished_sessions = df_session_finished.groupby("participant_id").count()["esm_session"].rename("finished_sessions")
# %%
df_adherence = baseline_inactive[["username", "gender", "age", "startlanguage"]].merge(df_esm_preprocessed[["username", "participant_id"]].drop_duplicates(), how="left", on="username")
df_adherence = df_adherence.merge(df_participant_finished_sessions, how="left", left_on="participant_id", right_index=True)
# %% tags=[]
df_adherence
# %%
df_esm_preprocessed_adherence = df_esm_preprocessed.merge(df_session_counts.reset_index(), how="left", on=["participant_id", "device_id", "esm_session"])
#df_esm_finished = df_esm_preprocessed_adherence[df_esm_preprocessed_adherence["session_response"]=="esm_finished"]
# %%
df_adherence = df_adherence.merge(df_esm_preprocessed_adherence[df_esm_preprocessed_adherence["session_response"] == "esm_finished"].groupby("participant_id").count()["session_response"], how="left", on="participant_id")
# %%
lm_adherence = ols('session_response ~ C(gender, Sum) + C(startlanguage, Sum)', data=df_adherence).fit()
lm_adherence = smf.ols('finished_sessions ~ C(gender) + C(startlanguage) + age', data=df_adherence).fit()
table = sm.stats.anova_lm(lm_adherence, typ=2) # Type 2 ANOVA DataFrame
print(table)
# %%
lr_ols = smf.ols('finished_sessions ~ C(gender) + C(startlanguage) + age', data=df_adherence)
ls_result = lr_ols.fit()
ls_result.summary()