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
根据2013—2016年南海两艘灯光罩网渔船的生产统计资料,结合卫星遥感获取的环境因子数据,运用广义可加模型(GAM)分析了南海春季鸢乌贼渔场分布及其与时空和环境因子的关系。结果表明:2013—2014年鸢乌贼单位捕捞努力量渔获量(CPUE,Catch...根据2013—2016年南海两艘灯光罩网渔船的生产统计资料,结合卫星遥感获取的环境因子数据,运用广义可加模型(GAM)分析了南海春季鸢乌贼渔场分布及其与时空和环境因子的关系。结果表明:2013—2014年鸢乌贼单位捕捞努力量渔获量(CPUE,Catch Per Unit Effort)呈增长趋势,而2015—2016年CPUE明显下降。2013—2015年鸢乌贼中心渔场主要分布在114°E—115°E,10°N—12°N区域,而2016年中心渔场向西偏移;GAM模型对CPUE的总偏差解释率为66.40%,其中经度、纬度、海表温度和叶绿素浓度4个因子与CPUE显著相关(P<0.05),影响因子按重要性排列,从大到小依次为:经度、纬度、叶绿素浓度和海表温度。而年份、月份和海表盐度对CPUE影响不显著(P>0.05)。鸢乌贼适宜海表温度为27℃~30℃,适宜叶绿素浓度为0.10~0.15 mg/m^(3)。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.
文摘根据2013—2016年南海两艘灯光罩网渔船的生产统计资料,结合卫星遥感获取的环境因子数据,运用广义可加模型(GAM)分析了南海春季鸢乌贼渔场分布及其与时空和环境因子的关系。结果表明:2013—2014年鸢乌贼单位捕捞努力量渔获量(CPUE,Catch Per Unit Effort)呈增长趋势,而2015—2016年CPUE明显下降。2013—2015年鸢乌贼中心渔场主要分布在114°E—115°E,10°N—12°N区域,而2016年中心渔场向西偏移;GAM模型对CPUE的总偏差解释率为66.40%,其中经度、纬度、海表温度和叶绿素浓度4个因子与CPUE显著相关(P<0.05),影响因子按重要性排列,从大到小依次为:经度、纬度、叶绿素浓度和海表温度。而年份、月份和海表盐度对CPUE影响不显著(P>0.05)。鸢乌贼适宜海表温度为27℃~30℃,适宜叶绿素浓度为0.10~0.15 mg/m^(3)。
