2024_Kalantari_PRR/code/scripts/behavior_time_statistics.py

import os
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import statsmodels.api as sm
from statsmodels.formula.api import mixedlm
from scikit_posthocs import posthoc_dunnett
from scipy.stats import dunnett
import statsmodels.formula.api as smf
import nipype as nip
import numpy as np

# Get the directory where the code file is located
code_dir = os.path.dirname(os.path.abspath(__file__))
# Get the parent directory of the code directory
parent_dir = os.path.dirname(code_dir)

# Specify the input file path relative to the code file
input_file_path = os.path.join(parent_dir, 'output', 'behavior_data_processed.csv')

# Read the CSV file into a Pandas DataFrame
df = pd.read_csv(input_file_path)

# Create an empty dictionary to store Dunnett's test results
dunnett_results = {}
tests = ["averageScore"]

import pandas as pd
import statsmodels.api as sm
from statsmodels.regression.mixed_linear_model import MixedLM

# Define the model formula
formula = "averageScore ~ TimePoint_merged"

filtered_df = df[df["Group"]=="Stroke"]
# Apply log transformation to the 'averageScore' variable
filtered_df["log_averageScore"] = np.log(filtered_df['averageScore'])

# Fit the mixed model
mixed_model = mixedlm(formula, filtered_df, groups='TimePoint_merged')
mixed_model_results = mixed_model.fit()

# Plot Q-Q plot of residuals
sm.qqplot(mixed_model_results.resid, line ='q')
plt.title('Q-Q Plot of Residuals')
plt.show()

# Print model summary
print(mixed_model_results.summary())