R语言是怎么做方差分解的

发布时间：2021-11-22 15:53:21 阅读：1018 作者：iii 栏目：大数据

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本篇内容介绍了“R语言是怎么做方差分解的”的有关知识，在实际案例的操作过程中，不少人都会遇到这样的困境，接下来就让小编带领大家学习一下如何处理这些情况吧！希望大家仔细阅读，能够学有所成！

首先是读入数据

datatotal<-read.table("datasetmultifunctionality.txt", header=T, sep="\t")colnames(datatotal)

接下来的代码是对数据进行转化

有的是常规的标准化

有的是log转化

常规的标准化开头提到的推文里介绍了方差分解必须用标准化后的数据，但是有的log转化是什么意思呢？

#####logtransformation momentsdatatotal[,c(12,13,16,17)]<-log(datatotal[,c(12,13,16,17)])datatotal[,14]<-log(datatotal[,14]-min(datatotal[,14])+1)datatotal[,15]<-log(datatotal[,15]-min(datatotal[,15])+1)datatotal[,18]<-log(datatotal[,18]-min(datatotal[,18])+1)datatotal[,19]<-log(datatotal[,19]-min(datatotal[,19])+1)#####Zscorring environmental variablesdatatotal$ELEVATION<-(datatotal$ELEVATION-mean(datatotal$ELEVATION))/sd(datatotal$ELEVATION)datatotal$LAT<-(datatotal$LAT-mean(datatotal$LAT))/sd(datatotal$LAT)datatotal$SINLONG<-(datatotal$SINLONG-mean(datatotal$SINLONG))/sd(datatotal$SINLONG)datatotal$COSLONG<-(datatotal$COSLONG-mean(datatotal$COSLONG))/sd(datatotal$COSLONG)datatotal$SLO<-(datatotal$SLO-mean(datatotal$SLO))/sd(datatotal$SLO)datatotal$ARIDITY<-(datatotal$ARIDITY-mean(datatotal$ARIDITY))/sd(datatotal$ARIDITY)datatotal$SAND<-(datatotal$SAND-mean(datatotal$SAND))/sd(datatotal$SAND)datatotal$PH<-(datatotal$PH-mean(datatotal$PH))/sd(datatotal$PH)datatotal$SR<-(datatotal$SR-mean(datatotal$SR))/sd(datatotal$SR)#####Zscorring momentsdatatotal$CWM_logH<-(datatotal$CWM_logH-mean(datatotal$CWM_logH))/sd(datatotal$CWM_logH)datatotal$CWV_logH<-(datatotal$CWV_logH-mean(datatotal$CWV_logH))/sd(datatotal$CWV_logH)datatotal$CWS_logH<-(datatotal$CWS_logH-mean(datatotal$CWS_logH))/sd(datatotal$CWS_logH)datatotal$CWK_logH<-(datatotal$CWK_logH-mean(datatotal$CWK_logH))/sd(datatotal$CWK_logH)datatotal$CWM_logSLA<-(datatotal$CWM_logSLA-mean(datatotal$CWM_logSLA))/sd(datatotal$CWM_logSLA)datatotal$CWV_logSLA<-(datatotal$CWV_logSLA-mean(datatotal$CWV_logSLA))/sd(datatotal$CWV_logSLA)datatotal$CWS_logSLA<-(datatotal$CWS_logSLA-mean(datatotal$CWS_logSLA))/sd(datatotal$CWS_logSLA)datatotal$CWK_logSLA<-(datatotal$CWK_logSLA-mean(datatotal$CWK_logSLA))/sd(datatotal$CWK_logSLA)#####Zscorring ecosystem functionsdatatotal$BGL<-(datatotal$BGL-mean(datatotal$BGL))/sd(datatotal$BGL)datatotal$FOS<-(datatotal$FOS-mean(datatotal$FOS))/sd(datatotal$FOS)datatotal$AMP<-(datatotal$AMP-mean(datatotal$AMP))/sd(datatotal$AMP)datatotal$NTR<-(datatotal$NTR-mean(datatotal$NTR))/sd(datatotal$NTR)datatotal$I.NDVI<-(datatotal$I.NDVI-mean(datatotal$I.NDVI))/sd(datatotal$I.NDVI)#####Calculating indices of multifunctionality (M5: 5 functions)colnames(datatotal)M5<-rowMeans(datatotal[,c(20,21,22,23,24)])datatotal<-cbind(datatotal,M5)#####Log-transfromation of multifunctionalitylogM5<-log(datatotal$M5-min(datatotal$M5)+1)datatotal<-cbind(datatotal,logM5)

加载 MuMIn这个包做模型选择

代码是

library(MuMIn)mod12<-lm(logM5 ~ LAT + SINLONG + COSLONG +               ARIDITY + SLO + SAND + PH + I(PH^2) + ELEVATION+            CWM_logSLA + I(CWM_logSLA^2)+ CWV_logSLA + I(CWV_logSLA^2) +  CWS_logSLA + CWK_logSLA + I(CWK_logSLA^2) +            CWM_logH + I(CWM_logH^2)+ CWV_logH + I(CWV_logH^2) +  CWS_logH + CWK_logH + I(CWK_logH^2) +            SR          , data=datatotal)# 这一步要好长时间dd12<-dredge(mod12, subset = ~ LAT & SINLONG & COSLONG & ARIDITY & SLO & SAND & PH &SR & ELEVATION &                  dc(CWM_logSLA,I(CWM_logSLA^2)) & dc(CWV_logSLA,I(CWV_logSLA^2)) & dc(CWK_logSLA,I(CWK_logSLA^2))              & dc(CWM_logH,I(CWM_logH^2)) & dc(CWV_logH,I(CWV_logH^2)) & dc(CWK_logH,I(CWK_logH^2)),              options(na.action = "na.fail"))subset(dd12,delta<2)de12<-model.avg(dd12, subset = delta < 2)summary(de12)