Department of Psychology, University of Otago, Dunedin, New Zealand.
BMC Public Health. 2021 Apr 9;21(1):695. doi: 10.1186/s12889-021-10690-3.
Variable selection is an important issue in many fields such as public health and psychology. Researchers often gather data on many variables of interest and then are faced with two challenging goals: building an accurate model with few predictors, and making probabilistic statements (inference) about this model. Unfortunately, it is currently difficult to attain these goals with the two most popular methods for variable selection methods: stepwise selection and LASSO. The aim of the present study was to demonstrate the use predictive projection feature selection - a novel Bayesian variable selection method that delivers both predictive power and inference. We apply predictive projection to a sample of New Zealand young adults, use it to build a compact model for predicting well-being, and compare it to other variable selection methods.
The sample consisted of 791 young adults (ages 18 to 25, 71.7% female) living in Dunedin, New Zealand who had taken part in the Daily Life Study in 2013-2014. Participants completed a 13-day online daily diary assessment of their well-being and a range of lifestyle variables (e.g., sleep, physical activity, diet variables). The participants' diary data was averaged across days and analyzed cross-sectionally to identify predictors of average flourishing. Predictive projection was used to select as few predictors as necessary to approximate the predictive accuracy of a reference model with all 28 predictors. Predictive projection was also compared to other variable selection methods, including stepwise selection and LASSO.
Three predictors were sufficient to approximate the predictions of the reference model: higher sleep quality, less trouble concentrating, and more servings of fruit. The performance of the projected submodel generalized well. Compared to other variable selection methods, predictive projection produced models with either matching or slightly worse performance; however, this performance was achieved with much fewer predictors.
Predictive projection was used to efficiently arrive at a compact model with good predictive accuracy. The predictors selected into the submodel - felt refreshed after waking up, had less trouble concentrating, and ate more servings of fruit - were all theoretically meaningful. Our findings showcase the utility of predictive projection in a practical variable selection problem.
变量选择是公共卫生和心理学等许多领域的重要问题。研究人员通常会收集许多感兴趣的变量的数据，然后面临两个具有挑战性的目标：用少数预测因子建立一个准确的模型，以及对这个模型进行概率推断（推断）。不幸的是，目前使用两种最流行的变量选择方法（逐步选择和 LASSO）很难实现这些目标。本研究的目的是展示预测投影特征选择的使用 - 一种新颖的贝叶斯变量选择方法，它提供了预测能力和推断。我们将预测投影应用于新西兰年轻成年人的样本中，用它来建立一个预测幸福感的紧凑模型，并将其与其他变量选择方法进行比较。
该样本包括 791 名年龄在 18 至 25 岁之间的年轻成年人（71.7%为女性），他们参加了 2013-2014 年在达尼丁进行的日常生活研究。参与者完成了为期 13 天的在线日常日记评估他们的幸福感和一系列生活方式变量（例如，睡眠、体育活动、饮食变量）。参与者的日记数据按天平均，进行横断面分析，以确定平均繁荣的预测因子。预测投影用于选择尽可能少的预测因子，以近似具有所有 28 个预测因子的参考模型的预测精度。预测投影也与其他变量选择方法进行了比较，包括逐步选择和 LASSO。
需要三个预测因子才能近似参考模型的预测：更高的睡眠质量、更少的注意力不集中和更多的水果份量。预测子模型的性能很好地概括了。与其他变量选择方法相比，预测投影产生的模型具有匹配或略差的性能；然而，这是通过使用更少的预测因子来实现的。
预测投影被用于有效地获得具有良好预测精度的紧凑模型。选择进入子模型的预测因子 - 醒来后感到神清气爽、注意力不集中较少、吃更多份水果 - 都具有理论意义。我们的研究结果展示了预测投影在实际变量选择问题中的实用性。
