#%% import pandas as pd import pandas as pd data = pd.read_csv("D:\\NN\\Data\\Study1AllUsers\\Cleaned_TrialResultsFull.csv") # 分组并计算均值 grouped_data = data.groupby(['ParticipantID']).agg({ 'AngularDistanceHMD': 'mean', 'AngularDistanceHand': 'mean', 'AngularDistanceLeye': 'mean' }).reset_index() print(grouped_data) output_path = 'D:\\NN\\Data\\Study1AllUsers\\ModalityAnalyse.csv' # 替换为你的输出文件路径 grouped_data.to_csv(output_path) # # 创建一个唯一的条件列，将 Depth, Theta, Width, Position 的组合转为单一标识符 # grouped_data['Condition'] = grouped_data['Depth'].astype(str) + '_' + grouped_data['Theta'].astype(str) + '_' + grouped_data['Width'].astype(str) + '_' + grouped_data['Position'].astype(str) # # 转换数据为宽格式 # wide_data = grouped_data.pivot_table(index='ParticipantID', # columns='Condition', # values=['MovementTime', 'AngularDistanceHMD', 'AngularDistanceHand', 'AngularDistanceLeye']) # # # 为了更好地兼容性，重命名列 # wide_data.columns = ['_'.join(col).strip() for col in wide_data.columns.values] # # # 输出查看转换后的数据 # print(wide_data.head()) # # # 保存为CSV文件，以便于导入SPSS # output_path = 'path_to_your_output_file.csv' # 替换为你的输出文件路径 # wide_data.to_csv(output_path) #%% import pandas as pd import numpy as np from statsmodels.stats.correlation_tools import cov_nearest from scipy.stats import chi2 # Load your data data = pd.read_csv("D:\\NN\\Data\\Study1AllUsers\\Cleaned_TrialResultsFull.csv") # data['Depth'] = data['Depth'].astype(str) # data['Theta'] = data['Theta'].astype(str) # data['Width'] = data['Width'].astype(str) # data['Position'] = data['Position'].astype(str) # columns = ['ParticipantID', 'BlockID', 'TrialID', 'MovementTime', 'Depth', 'Theta', 'Width','Position'] columns = ['ParticipantID', 'BlockID', 'TrialID', 'MovementTime','AngularDistanceHMD','AngularDistanceHand','AngularDistanceLeye', 'Depth', 'Theta', 'Width','Position'] # Aggregating data for each user under each condition data= data[columns] grouped_data = data.groupby(['ParticipantID', 'Depth', 'Theta', 'Width','Position']).agg({ 'AngularDistanceLeye': 'mean' }).reset_index() # 创建一个唯一的条件列，将 Depth, Theta, Width, Position 的组合转为单一标识符 grouped_data['Condition'] = grouped_data['Depth'].astype(str) + '_' + grouped_data['Theta'].astype(str) + '_' + grouped_data['Width'].astype(str)+ '_' + grouped_data['Position'].astype(str) # 转换数据为宽格式 wide_data = grouped_data.pivot_table(index='ParticipantID', columns='Condition', values=['AngularDistanceLeye']) # 为了更好地兼容性，重命名列 wide_data.columns = ['_'.join(col).strip() for col in wide_data.columns.values] # 输出查看转换后的数据 print(wide_data.head()) output_path = 'D:\\NN\\Data\\Study1AllUsers\\EyeDistance.csv' # 替换为你的输出文件路径 wide_data.to_csv(output_path) # output_path = 'D:\\NN\\Data\\Study1AllUsers\\Cleaned_TrialResultsFull1.csv' # 替换为你的输出文件路径 # grouped_data.to_csv(output_path, index=False) # grouped_data = data.groupby(['ParticipantID', 'Depth', 'Theta', 'Width', 'Position']).agg({ # 'MovementTime': 'mean', # 'AngularDistanceHMD': 'mean', # 'AngularDistanceHand': 'mean', # 'AngularDistanceLeye': 'mean' # }).reset_index() # #%% # import pandas as pd # import numpy as np # from sklearn.linear_model import LinearRegression # import matplotlib.pyplot as plt # # # Correcting the data based on the user's indication # data_corrected = { # "Theta": [10, 10, 15, 15, 20, 20, 25, 25, 50, 50, 75, 75], # "Width": [4.5, 9.0, 4.5, 9.0, 4.5, 9.0, 4.5, 9.0, 4.5, 9.0, 4.5, 9.0], # "Mean": [704.222126, 508.689598, 797.962563, 560.906088, 904.062486, 646.458888, # 1183.485047, 796.196496, 1464.353523, 1034.035743, 1728.876132, 1266.901965] # } # # # model = LinearRegression() # # df_corrected = pd.DataFrame(data_corrected) # # # Compute the index of difficulty again # df_corrected['ID'] = np.log2(df_corrected['Theta'] / df_corrected['Width'] + 1) # # # Re-run linear regression # model.fit(df_corrected[['ID']], df_corrected['Mean']) # # # Predict values using the fitted model # df_corrected['Predicted'] = model.predict(df_corrected[['ID']]) # a_corrected = model.intercept_ # b_corrected = model.coef_[0] # # # Recalculate R-squared value # r_squared_corrected = model.score(df_corrected[['ID']], df_corrected['Mean']) # # # Plotting the corrected data # plt.figure(figsize=(16, 9)) # plt.scatter(df_corrected['ID'], df_corrected['Mean'], color='blue', label='Observed Data') # plt.plot(df_corrected['ID'], df_corrected['Predicted'], color='darkblue', linestyle='dashed', label='Fitted Line') # # plt.xlabel('Index of Difficulty (bits)') # plt.ylabel('Movement Time (ms)') # plt.grid(True) # plt.legend() # plt.text(3.5, 1100, f'R² = {r_squared_corrected:.4f}', fontsize=12) # # plt.show(), (a_corrected, b_corrected, r_squared_corrected) import pandas as pd #%% import pandas as pd from statsmodels.stats.anova import AnovaRM import statsmodels.api as sm # 加载数据 # 读取数据 data = pd.read_csv("D:\\NN\\Data\\Study1AllUsers\\Cleaned_TrialResultsFull.csv") # 确保分类变量为字符串格式 data['Depth'] = data['Depth'].astype(str) data['Theta'] = data['Theta'].astype(str) data['Width'] = data['Width'].astype(str) # 筛选掉特定参与者的数据 filtered_data = data[~data['ParticipantID'].isin([3, 6, 15, 19, 18, 20, 22])] # 重新进行数据聚合 filtered_aggregated_data = filtered_data.groupby(['ParticipantID', 'Depth', 'Theta', 'Width']).mean().reset_index() print(filtered_aggregated_data) # 执行重复测量ANOVA，并应用Greenhouse-Geisser校正 rm_anova_results = AnovaRM(filtered_aggregated_data, 'MovementTime', 'ParticipantID', within=['Depth', 'Theta', 'Width']) # 打印ANOVA结果摘要 print(rm_anova_results.summary()) # 计算eta squared anova_table = rm_anova_results.anova_table anova_table['eta_squared'] = (anova_table['F Value'] * anova_table['Num DF']) / \ (anova_table['F Value'] * anova_table['Num DF'] + anova_table['Den DF']) # 打印带有eta squared的结果表格 print(anova_table[['F Value', 'Pr > F', 'eta_squared']]) #%% from statsmodels.stats.multicomp import pairwise_tukeyhsd # Prepare the data for Tukey HSD tests tukey_data = filtered_aggregated_data[['Theta', 'Width', 'MovementTime']] # Perform Tukey HSD test for Theta tukey_result_theta = pairwise_tukeyhsd(endog=tukey_data['MovementTime'], groups=tukey_data['Theta'], alpha=0.05) # Perform Tukey HSD test for Width tukey_result_width = pairwise_tukeyhsd(endog=tukey_data['MovementTime'], groups=tukey_data['Width'], alpha=0.05) tukey_result_theta.summary(), tukey_result_width.summary() #%% print(tukey_result_theta.summary()) print(tukey_result_width.summary()) #%% for width_level in filtered_aggregated_data['Width'].unique(): subset = filtered_aggregated_data[filtered_aggregated_data['Width'] == width_level] print(f'Tukey HSD for Width {width_level}:') print(pairwise_tukeyhsd(subset['MovementTime'], subset['Theta'], alpha=0.05).summary()) # 遍历每个Theta水平 for theta_level in filtered_aggregated_data['Theta'].unique(): subset = filtered_aggregated_data[filtered_aggregated_data['Theta'] == theta_level] print(f'Tukey HSD for Theta {theta_level}:') print(pairwise_tukeyhsd(subset['MovementTime'], subset['Width'], alpha=0.05).summary()) #%% import pandas as pd from statsmodels.stats.anova import AnovaRM # 加载数据 data = pd.read_csv("D:\\NN\\Data\\Study1AllUsers\\TrialResultsFull.csv") # 选择需要分析的列，并确保分类变量为字符串格式 data['Depth'] = data['Depth'].astype(str) data['Theta'] = data['Theta'].astype(str) data['Width'] = data['Width'].astype(str) # 筛选掉特定参与者的数据 filtered_data = data[~data['ParticipantID'].isin([3, 6, 15, 19, 18, 20, 22])] filtered_aggregated_data = filtered_data.groupby(['ParticipantID', 'Depth', 'Theta', 'Width', 'Position']).mean().reset_index() print(filtered_aggregated_data) # 执行重复测量ANOVA rm_anova_results = AnovaRM(filtered_aggregated_data, 'AngularDistanceHand', 'ParticipantID', within=['Depth', 'Theta', 'Width', 'Position']).fit() print(rm_anova_results.summary()) anova_table = rm_anova_results.anova_table anova_table['eta_squared'] = (anova_table['F Value'] * anova_table['Num DF']) / \ (anova_table['F Value'] * anova_table['Num DF'] + anova_table['Den DF']) print(anova_table[['F Value', 'Pr > F', 'eta_squared']])