与其他SEM技术相比,PLS允许研究人员同时估计复杂的相互关系,这些关系涉及多种构念和指标及其直接、间接或调节关系,否则这些关系不容易解开和检查。 此外,PLS-SEM的应用提供了研究者一系列先进的工具(例如FIMIX-PLS,CTA-PLS,IPMA,PLSpredict),如果使用得...
(1) The Q² value in PLSpredict compares the prediction errors of the PLS path model against simple mean predictions. For this purpose, it uses the mean value of the training sample to predict the outcomes of the holdout sample. The Q² value results interpretation is similar to the as...
与其他的SEM技术相比,PLS允许研究人员同时估计复杂的相互关系,这些关系涉及多种构念和指标及其直接、间接或干扰关系,否则这些关系不容易解释和检查。此外,PLS-SEM的应用还提供了研究者一系列先进的工具(例如FIMIX-PLS,CTA-PLS,IPMA,PLSpredict),如果使用得当,它们不仅可以让研究者为社会科学领域做出重要的理论贡献,而且...
R语言: 使用pls包或caret包构建PLS模型后,可以使用summary()函数或直接计算上述指标。 library(pls) model <- plsr(Y ~ X, data = train_data, validation = "CV") predictions <- predict(model, newdata = test_data) RMSEp <- sqrt(mean((test_data$Y - predictions)^2)) MAEp <- mean(abs(test...
PLSpredict is a library of tools to perform Partial Least Squares Path Modelling and Prediction in R. This readme serves to explain the working of the PLSpredict package and to provide an example as to the intended use. To that end, we have provided a sample dataset, AnimData.csv, to wh...
PLSpredictOut-of-sample predictionPredictive powerPurpose Partial least squares (PLS) has been introduced as a "causal-predictive" approach to structural equation modeling (SEM), designed to overcome the apparent dichotomy between explanation and prediction. However, while researchers using PLS-SEM ...
2024年7月6日，SmartPLS推出了新版本4.1.0.6，为用户带来了显著的功能改进和细节修正。此次更新继续体现了SmartPLS团队致力于优化软件性能和用户体验的承诺。主要更新内容 图表坐标轴自定义：用户现在可以调整图表坐标轴的蕞小值和蕞大值，实现更加准确的数据展示和分析。新增指标平均值（IA）：在PLSPredict中增加了...
[10,20,30]# 划分训练集和测试集X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=42)# 建立PLS模型pls=PLSRegression(n_components=2)pls.fit(X_train,y_train)# 预测y_pred=pls.predict(X_test)# 评估模型r2=r2_score(y_test,y_pred)print("R2 Score: ",r2...
PLSpredictout-of-sample predictionpredictive powerPurpose - Partial least squares (PLS) has been introduced as a "causal-predictive" approach to structural equation modeling (SEM), designed to overcome the appaShmueli, GalitSarstedt, MarkoHair, Joseph F....
6. PLSpredict功能与范例展示 2024/1/23 1. Importance-Performance Map Analysis (IPMA) 2. 控制变量 (Control variable) 的设定与评估 3. 已发表的SSCI期刊与PLS应用实例 4. 经典PLS文章分享 5. 人工智能 (AI) 是否能协助产生写作素材? 6. 生成式AI人工智能协作的工具介绍 7. 量化分析与写作素材有关的pr...