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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!
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datatotal<-read.table("datasetmultifunctionality.txt", header=T, sep="\t")
colnames(datatotal)
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 moments
datatotal[,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 variables
datatotal$ELEVATION<-(datatotal$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 moments
datatotal$CWM_logH<-(datatotal$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 functions
datatotal$BGL<-(datatotal$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 multifunctionality
logM5<-log(datatotal$M5-min(datatotal$M5)+1)
datatotal<-cbind(datatotal,logM5)
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)
这一步得到的数据就是论文中 的figure4a
下期推文介绍如何利用得到的数据画图
这里遇到的问题是:
I()
函数包起来,这个函数起到什么作用呢?dc()
函数,这个函数又起到什么作用呢?“R语言是怎么做方差分解的”的内容就介绍到这里了,感谢大家的阅读。如果想了解更多行业相关的知识可以关注创新互联网站,小编将为大家输出更多高质量的实用文章!
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