结构方程建模
分类
选型
选择(遗传算法)
计算机科学
领域
一套
生态学
人工智能
数据挖掘
算法
机器学习
地理
生物
情报检索
考古
作者
Mario Garrido,Scott K. Hansen,Rami Yaari,Hadas Hawlena
标识
DOI:10.1111/2041-210x.13742
摘要
Abstract Structural equation modelling (SEM) can illuminate complex interaction networks of the sort found in ecology. However, selecting optimally complex, data‐supported SEM models and quantifying their uncertainty are difficult processes. To this end, we recommend a formal model selection approach (MSA) that uses information criteria. Using a suite of numerical simulations, we compare MSA‐SEM against two traditional methods. We find that MSA‐SEM exhibits superior, unbiased results under the suboptimal realistic conditions characteristic of ecological studies. We then provide a road map for MSA‐SEM and demonstrate its use via a case study. We illustrate the unique abilities of SEM to confirm a network structure within the realm of known causal pathways and delineate the boundaries within which MSA‐SEM should be applied.
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