Generalized Additive Models(GAMs)are widely employed in ecological research,serving as a powerful tool for ecologists to explore complex nonlinear relationships between a response variable and predictors.Nevertheless,...Generalized Additive Models(GAMs)are widely employed in ecological research,serving as a powerful tool for ecologists to explore complex nonlinear relationships between a response variable and predictors.Nevertheless,evaluating the relative importance of predictors with concurvity(analogous to collinearity)on response variables in GAMs remains a challenge.To address this challenge,we developed an R package named gam.hp.gam.hp calculates individual R^(2) values for predictors,based on the concept of'average shared variance',a method previously introduced for multiple regression and canonical analyses.Through these individual R^(2)s,which add up to the overall R^(2),researchers can evaluate the relative importance of each predictor within GAMs.We illustrate the utility of the gam.hp package by evaluating the relative importance of emission sources and meteorological factors in explaining ozone concentration variability in air quality data from London,UK.We believe that the gam.hp package will improve the interpretation of results obtained from GAMs.展开更多
为分析环境因子对薇甘菊Mikania micrantha分布的影响,2020—2021年间以中山市为研究区,于薇甘菊盛花期进行实地踏查,收集薇甘菊分布点数据,定量分析10个环境因子对薇甘菊分布的影响,基于GAM模型(Generalized additive model)对中山市...为分析环境因子对薇甘菊Mikania micrantha分布的影响,2020—2021年间以中山市为研究区,于薇甘菊盛花期进行实地踏查,收集薇甘菊分布点数据,定量分析10个环境因子对薇甘菊分布的影响,基于GAM模型(Generalized additive model)对中山市薇甘菊适生区分布进行预测。结果显示,(1)模型结果拟合精度高,TSS(Total sum of squares)均值为0.87,AUC(Area under the curve)均值为0.93;(2)10个环境因子对薇甘菊分布均有贡献,贡献率最大的为降水量季节性变化(18.63%),其次为海拔(17.90%),第三为4月降水量(16.47%);(3)模型预测结果显示中山市约89.23%的地区适宜薇甘菊分布。研究构建的GAM模型拟合精度高,并证明了中山市区域尺度下水、热和海拔为影响薇甘菊分布的主导因子。展开更多
基金supported by the National Natural Science Foundation of China (32271551)National Key Research and Development Program of China (2023YFF0805803)the Metasequoia funding of Nanjing Forestry University。
文摘Generalized Additive Models(GAMs)are widely employed in ecological research,serving as a powerful tool for ecologists to explore complex nonlinear relationships between a response variable and predictors.Nevertheless,evaluating the relative importance of predictors with concurvity(analogous to collinearity)on response variables in GAMs remains a challenge.To address this challenge,we developed an R package named gam.hp.gam.hp calculates individual R^(2) values for predictors,based on the concept of'average shared variance',a method previously introduced for multiple regression and canonical analyses.Through these individual R^(2)s,which add up to the overall R^(2),researchers can evaluate the relative importance of each predictor within GAMs.We illustrate the utility of the gam.hp package by evaluating the relative importance of emission sources and meteorological factors in explaining ozone concentration variability in air quality data from London,UK.We believe that the gam.hp package will improve the interpretation of results obtained from GAMs.
文摘为分析环境因子对薇甘菊Mikania micrantha分布的影响,2020—2021年间以中山市为研究区,于薇甘菊盛花期进行实地踏查,收集薇甘菊分布点数据,定量分析10个环境因子对薇甘菊分布的影响,基于GAM模型(Generalized additive model)对中山市薇甘菊适生区分布进行预测。结果显示,(1)模型结果拟合精度高,TSS(Total sum of squares)均值为0.87,AUC(Area under the curve)均值为0.93;(2)10个环境因子对薇甘菊分布均有贡献,贡献率最大的为降水量季节性变化(18.63%),其次为海拔(17.90%),第三为4月降水量(16.47%);(3)模型预测结果显示中山市约89.23%的地区适宜薇甘菊分布。研究构建的GAM模型拟合精度高,并证明了中山市区域尺度下水、热和海拔为影响薇甘菊分布的主导因子。
