之前好多人在公众号留言问这个 方差分解 的内容，但是之前自己也没有听说过。最近看到有人分享了公众号推文 一种简单易行的方差分解方法。看了这个推文我目前理解的是 方差分解的主要作用是 量化回归模型Y=b0+b1x1+b2x2+…中x1, x2, x3…对Y贡献的相对大小，以及不同X所属的因素类别（如生物因素，非生物因素）对Y的贡献大小。
这篇推文以已经发表的论文中的数据为例子进行了介绍，论文是
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这篇论文关于方差分解的内容数据代码是公开的，下载链接是
https://figshare.com/s/053837c4fa852f035448
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我看了这些代码，有的地方还看不明白，但是利用数据能够跑通流程，今天先记录一下，后面抽时间再看，有什么新的理解再来记录
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 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)
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)
image.png
image.png
这一步得到的数据就是论文中 的figure4a
image.png
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：
- 1、 模型里有的变量会用
I()函数包起来，这个函数起到什么作用呢？ - 2、模型选择那一步用到了
dc()函数，这个函数又起到什么作用呢？
今天的内容就到这里了
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今天的内容主要参考
- 公众号 二傻统计 的推文 一种简单易行的方差分解方法
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