如何从列表中绘制R中具有背景（splsda模型）的单个观测值的多个图？

我在R中对10个数据帧（10个研究区域的数据）执行splsda模型，存储为列表（

datalist

）。所有这些数据帧都是相似的，具有相同的变量，但只是不同的值

> head(datalist[[1]])
  OID POINTID WETLAND TPI200 TPI350 TPI500 TPI700 TPI900 TPI1000 TPI2000 TPI3000 TPI4000 TPI5000 TPI2500
1  -1       1 no wetl     70     67     55     50     48      46      53      47      49      63      48
2  -1       2 no wetl     37     42     35     29     32      16      17      35      49      63      26
3  -1       3 no wetl     45     55     45     39     41      41      53      47      49      63      48
4  -1       4 no wetl     46     58     51     43     46      36      54      47      49      62      49
5  -1       5 no wetl     58     55     53     49     47      46      54      47      49      62      49
6  -1       6 no wetl     56     53     51     49     46      46      54      47      49      61      49

dput（datalist[[1]]）

可以找到（wetransfer中的txt文件）

我已经使用

lappy

使用两个组件执行了

splsda.model

，这将导致

model\u列表

：

library(mixOmics)

custom_splsda <- function(datalist, ncomp, keepX, ..., Xcols, Ycol){
  Y <- datalist[[Ycol]]
  X <- datalist[Xcols]
  res <- splsda(X, Y, ncomp = ncomp, keepX = keepX, ...)
  res
}

model_list <- lapply(datalist, custom_splsda, ncomp = 2, keepX = c(5, 5), Xcols = 4:8, Ycol = "WETLAND")

库（混合组学）
custom_splsda根据您提供的示例数据，这里提供了一个解决方案，用于将每个绘图保存在一个pdf中。为了使练习具有可复制性，我使用您添加的输入创建了
datalist
。在您真正的问题中，您必须具有带有名称的
datalist
，以便将标题添加到绘图中。另外，一些绘图数据不可用，如
datastudyarea1
，但为您完成这部分代码并不困难。这是草图：

library(mixOmics)
#Data
datalist <- list(df1 = structure(list(OID = c(-1, -1, -1, -1, -1, -1), POINTID = c(1, 
2, 3, 4, 5, 6), WETLAND = c("no wetl", "no wetl", "no wetl", 
"wetl", "wetl", "wetl"), TPI200 = c(70, 37, 45, 46, 58, 56), 
    TPI350 = c(67, 42, 55, 58, 55, 53), TPI500 = c(55, 35, 45, 
    51, 53, 51), TPI700 = c(50, 29, 39, 43, 49, 49), TPI900 = c(48, 
    32, 41, 46, 47, 46), TPI1000 = c(46, 16, 41, 36, 46, 46), 
    TPI2000 = c(53, 17, 53, 54, 54, 54), TPI3000 = c(47, 35, 
    47, 47, 47, 47), TPI4000 = c(49, 49, 49, 49, 49, 49), TPI5000 = c(63, 
    63, 63, 62, 62, 61), TPI2500 = c(48, 26, 48, 49, 49, 49)), row.names = c(NA, 
6L), class = "data.frame"), df2 = structure(list(OID = c(-1, 
-1, -1, -1, -1, -1), POINTID = c(1, 2, 3, 4, 5, 6), WETLAND = c("no wetl", 
"no wetl", "no wetl", "wetl", "wetl", "wetl"), TPI200 = c(70, 
37, 45, 46, 58, 56), TPI350 = c(67, 42, 55, 58, 55, 53), TPI500 = c(55, 
35, 45, 51, 53, 51), TPI700 = c(50, 29, 39, 43, 49, 49), TPI900 = c(48, 
32, 41, 46, 47, 46), TPI1000 = c(46, 16, 41, 36, 46, 46), TPI2000 = c(53, 
17, 53, 54, 54, 54), TPI3000 = c(47, 35, 47, 47, 47, 47), TPI4000 = c(49, 
49, 49, 49, 49, 49), TPI5000 = c(63, 63, 63, 62, 62, 61), TPI2500 = c(48, 
26, 48, 49, 49, 49)), row.names = c(NA, 6L), class = "data.frame"))

库（混合组学）
#资料
数据列表请
dput（数据列表）
或
dput（数据列表[[1]]）
使您的问题重现并帮助您@鸭子，数据列表太大了！所有数据帧都包含超过10000-20000行！我无法进行
dput（型号列表[[1]]）
！但是我编辑了这篇文章，并发布了
数据列表的第一个研究区域（第一个元素/数据框架）
！这有帮助吗？@还有你添加的数据，我能够回答你昨天发布的问题！您可以检查一下，看看这是否适合您：）
library(mixOmics)
#Data
datalist <- list(df1 = structure(list(OID = c(-1, -1, -1, -1, -1, -1), POINTID = c(1, 
2, 3, 4, 5, 6), WETLAND = c("no wetl", "no wetl", "no wetl", 
"wetl", "wetl", "wetl"), TPI200 = c(70, 37, 45, 46, 58, 56), 
    TPI350 = c(67, 42, 55, 58, 55, 53), TPI500 = c(55, 35, 45, 
    51, 53, 51), TPI700 = c(50, 29, 39, 43, 49, 49), TPI900 = c(48, 
    32, 41, 46, 47, 46), TPI1000 = c(46, 16, 41, 36, 46, 46), 
    TPI2000 = c(53, 17, 53, 54, 54, 54), TPI3000 = c(47, 35, 
    47, 47, 47, 47), TPI4000 = c(49, 49, 49, 49, 49, 49), TPI5000 = c(63, 
    63, 63, 62, 62, 61), TPI2500 = c(48, 26, 48, 49, 49, 49)), row.names = c(NA, 
6L), class = "data.frame"), df2 = structure(list(OID = c(-1, 
-1, -1, -1, -1, -1), POINTID = c(1, 2, 3, 4, 5, 6), WETLAND = c("no wetl", 
"no wetl", "no wetl", "wetl", "wetl", "wetl"), TPI200 = c(70, 
37, 45, 46, 58, 56), TPI350 = c(67, 42, 55, 58, 55, 53), TPI500 = c(55, 
35, 45, 51, 53, 51), TPI700 = c(50, 29, 39, 43, 49, 49), TPI900 = c(48, 
32, 41, 46, 47, 46), TPI1000 = c(46, 16, 41, 36, 46, 46), TPI2000 = c(53, 
17, 53, 54, 54, 54), TPI3000 = c(47, 35, 47, 47, 47, 47), TPI4000 = c(49, 
49, 49, 49, 49, 49), TPI5000 = c(63, 63, 63, 62, 62, 61), TPI2500 = c(48, 
26, 48, 49, 49, 49)), row.names = c(NA, 6L), class = "data.frame"))

#Function
custom_splsda <- function(datalist, ncomp, keepX, ..., Xcols, Ycol){
  Y <- datalist[[Ycol]]
  X <- datalist[Xcols]
  res <- splsda(X, Y, ncomp = ncomp, keepX = keepX, ...)
  res
}

#Create model_list, you must have the object created
model_list <- lapply(datalist, custom_splsda,
                     ncomp = 2, keepX = c(5, 5),
                     Xcols = 4:8, Ycol = "WETLAND")

#Iterate to save
#Create pdf
pdf('Summaryplots.pdf',width = 14)
for(i in 1:length(model_list))
{
  #Create background
  background <- background.predict(model_list[[i]], 
                                   comp.predicted = 2, 
                                   dist = "max.dist")
  #Plot
  plotIndiv(model_list[[i]], comp = 1:2,
            ind.names = FALSE, title = paste0("Maximum distance ",names(model_list)[i]),
            legend = TRUE,  background = background,cex = 0.5)
}
#Turn devices off
dev.off()