INTRODUCTION It is common in agronomic experiments to have data on a range of plant traits across treatments comprising levels of one factor,combinations of two or more factors,and/or repeat measures across time or so...INTRODUCTION It is common in agronomic experiments to have data on a range of plant traits across treatments comprising levels of one factor,combinations of two or more factors,and/or repeat measures across time or some other entity such as soil depth.In this case,traditional univariate ANOVA,which examines the measured traits one by one and is amenable to the development of complex statistical models,risks missing overarching patterns that might emerge if the data were analyzed as an interacting set where multivariate trait associations can be elucidated.Multivariate analyses,on the other hand,do consider multiple traits simultaneously but often struggle to accommodate complex treatment combinations.For more complex agronomic data sets,the writer has often used principal component analysis(PCA)as a data exploration and pattern detection tool,to identify the salient features of a data set from a multivariate perspective.This editorial aims to introduce PCA to readers unfamiliar with it,illustrate by example how PCA works,and to demonstrate the versatility of PCA by outlining some applications of PCA that the writer has developed for particular data sets during a 40‐year research career.It is not possible in a brief editorial to provide a textbook‐level and statistically robust coverage of the topic of PCA;detailed expositions of PCA have been produced by Joliffe(1986,2002)and many others.A motivation to write has been that I often see PCA results published in ways that reflect incomplete understanding of its mathematical properties and behavior.However,these notes are not intended as a substitute for consultation with a professional statistician.展开更多
文摘INTRODUCTION It is common in agronomic experiments to have data on a range of plant traits across treatments comprising levels of one factor,combinations of two or more factors,and/or repeat measures across time or some other entity such as soil depth.In this case,traditional univariate ANOVA,which examines the measured traits one by one and is amenable to the development of complex statistical models,risks missing overarching patterns that might emerge if the data were analyzed as an interacting set where multivariate trait associations can be elucidated.Multivariate analyses,on the other hand,do consider multiple traits simultaneously but often struggle to accommodate complex treatment combinations.For more complex agronomic data sets,the writer has often used principal component analysis(PCA)as a data exploration and pattern detection tool,to identify the salient features of a data set from a multivariate perspective.This editorial aims to introduce PCA to readers unfamiliar with it,illustrate by example how PCA works,and to demonstrate the versatility of PCA by outlining some applications of PCA that the writer has developed for particular data sets during a 40‐year research career.It is not possible in a brief editorial to provide a textbook‐level and statistically robust coverage of the topic of PCA;detailed expositions of PCA have been produced by Joliffe(1986,2002)and many others.A motivation to write has been that I often see PCA results published in ways that reflect incomplete understanding of its mathematical properties and behavior.However,these notes are not intended as a substitute for consultation with a professional statistician.
