方差表整理

获得的方差表是排在一列上的，需要整合成发表格式。

options(digits = 4)
var <- read.csv("D:/R/Spatial_Compete/results/che/res/var.csv")
str(var)

## 'data.frame':    140 obs. of  5 variables:
##  $ X                  : Factor w/ 5 levels "block","genetic",..: 3 2 4 3 2 5 4 3 2 5 ...
##  $ model              : Factor w/ 4 levels "ar","base","spl",..: 2 2 2 3 3 3 3 1 1 1 ...
##  $ trait              : Factor w/ 9 levels "alfacel","d05",..: 4 4 4 4 4 4 4 4 4 4 ...
##  $ Estimated.variances: num  1.336 0.957 1.633 0.58 0.338 ...
##  $ S.E.               : num  0.1 0.28 0.196 NA NA NA NA 0.044 0.11 0.116 ...

knitr::kable(head(var))

X	model	trait	Estimated.variances	S.E.
Plot	base	h05	1.336	0.100
genetic	base	h05	0.957	0.280
Residual	base	h05	1.633	0.196
Plot	spl	h05	0.580	NA
genetic	spl	h05	0.338	NA
spatial	spl	h05	5.953	NA

# 性状的个数
trait_name <- levels(var$trait)
var.temp <- matrix(,length(trait_name)*4,12)
for(trait.i in 1: length(trait_name)){
  var_trait <- subset(var,trait==trait_name[trait.i])
  # 模型的个数
  model_name <- levels(factor(var_trait$model))
  # 把单个性状内的所有模型结果行放到这个矩阵中，一共4个模型，5个组分5个se，11和12列分别放模型名称和性状名称
  var_trait.temp <- matrix(,4,12)
  for(model.i in 1:length(model_name)){
    var_trait_model <- subset(var_trait,model==model_name[model.i])
    # 一个单行空矩阵，把一个性状内一个模型的个组分估计及se放到一行中
    var_trait_model.temp <- matrix(,1,dim(var_trait_model)[1]*2)
    for(var.i in 1:dim(var_trait_model)[1]){
      var_trait_model.temp[1,(2*var.i-1):(2*var.i)] <- var_trait_model[var.i,4]
      var_trait_model.temp[1,(2*var.i)] <- var_trait_model[var.i,5]
    }
    var_trait.temp[model.i,1:dim(var_trait_model.temp)[2]] <- var_trait_model.temp[1,]
    # 有的模型没有结果，所以维度报错，这里假设缺失一个模型
    if (length(model_name)<4) model_name <- c(model_name,"NA")
    var_trait.temp[,11] <- as.matrix(model_name)
    var_trait.temp[,12] <-trait_name[trait.i]
  }
  var.temp[(trait.i*4-3):(trait.i*4),] <- var_trait.temp[1:4,]
}
var.temp <- as.data.frame(var.temp)
# 列名
colnames(var.temp) <- c(rep(c("Est","se"),5),"model","trait")
knitr::kable(head(var.temp))

Est	se	Est	se	Est	se	Est	se	Est	se	model	trait
3.757	0.196	4.962	0.068	0	0	0.183	0.003	NA	NA	ar	alfacel
3.757	0.383	4.962	0.263	0.183	0.075	NA	NA	NA	NA	base	alfacel
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	spl	alfacel
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	splar	alfacel
0.102	0.076	0.197	0.114	1.06	0.146	4.892	0.151	NA	NA	ar	d05
0.788	0.095	0.224	0.133	5.311	0.152	NA	NA	NA	NA	base	d05

# 结果保存路径
path_save_res <- "D:/R/Spatial_Compete/results/che/res/"
name_save <- paste(path_save_res, "var_adj.csv",sep="")
# write.csv(var.temp,name_save)