3

我这里有点麻烦，请帮帮我。我有这个数据

set.seed(4)
mydata <- data.frame(var = rnorm(100),
                     temp = rnorm(100),
                     subj = as.factor(rep(c(1:10),5)),
                     trt = rep(c("A","B"), 50))

和适合他们的模型

lm  <- lm(var ~ temp * subj, data = mydata)

我想用格子绘制结果，并通过它们拟合回归线，用我的模型预测。为此，我使用了这种方法，由 D. Sarkar 概述了“用于高级用户的格子技巧”

temp_rng <- range(mydata$temp, finite = TRUE)

grid <- expand.grid(temp = do.breaks(temp_rng, 30),
                    subj = unique(mydata$subj),
                    trt = unique(mydata$trt))

model <- cbind(grid, var = predict(lm, newdata = grid))

orig <- mydata[c("var","temp","subj","trt")]

combined <- make.groups(original = orig, model = model)


xyplot(var ~ temp | subj, 
       data = combined,
       groups = which,
       type = c("p", "l"),
       distribute.type = TRUE
       )

到目前为止，一切都很好，但我还想为两种处理的数据点分配填充颜色trt=1和trt=2.

所以我写了这段代码，效果很好，但是在绘制回归线时，面板功能似乎无法识别类型......

my.fill <- c("black", "grey")

plot <- with(combined,
        xyplot(var ~ temp | subj,
              data = combined,
              group = combined$which,
              type = c("p", "l"),
              distribute.type = TRUE,
              panel = function(x, y, ..., subscripts){
                     fill <- my.fill[combined$trt[subscripts]] 
                     panel.xyplot(x, y, pch = 21, fill = my.fill, col = "black")
                     },
             key = list(space = "right",
                     text = list(c("trt1", "trt2"), cex = 0.8),
                     points = list(pch = c(21), fill = c("black", "grey")),
                     rep = FALSE)
                     )
      )
plot

我还尝试在 中移动类型和分发类型panel.xyplot，以及panel.xyplot像这样对其中的数据进行子集化

plot <- with(combined,
        xyplot(var ~ temp | subj,
              data = combined,
              panel = function(x, y, ..., subscripts){
                     fill <- my.fill[combined$trt[subscripts]] 
                     panel.xyplot(x[combined$which=="original"], y[combined$which=="original"], pch = 21, fill = my.fill, col = "black")
                     panel.xyplot(x[combined$which=="model"], y[combined$which=="model"], type = "l", col = "black")
                     },
             key = list(space = "right",
                     text = list(c("trt1", "trt2"), cex = 0.8),
                     points = list(pch = c(21), fill = c("black", "grey")),
                     rep = FALSE)
                     )
      )
plot

但也没有成功。

谁能帮我将预测值绘制为一条线而不是点？

4

3 回答 3

6

这可能是latticeExtra包裹的工作。

library(latticeExtra)
p1 <- xyplot(var ~ temp | subj, data=orig, panel=function(..., subscripts) {
  fill <- my.fill[combined$trt[subscripts]] 
  panel.xyplot(..., pch=21, fill=my.fill, col="black")
})
p2 <- xyplot(var ~ temp | subj, data=model, type="l")
p1+p2

在此处输入图像描述

我不确定您的第一次尝试发生了什么，但是带有下标的那个不起作用，因为 x 和 y 是 subj 数据的子集，因此使用基于的向量对它们进行子集化combined将无法正常工作你认为会的。试试这个。

xyplot(var ~ temp | subj, groups=which, data = combined,
       panel = function(x, y, groups, subscripts){
         fill <- my.fill[combined$trt[subscripts]]
         g <- groups[subscripts]
         panel.points(x[g=="original"], y[g=="original"], pch = 21, 
                      fill = my.fill, col = "black")
         panel.lines(x[g=="model"], y[g=="model"], col = "black")
       },
       key = list(space = "right",
         text = list(c("trt1", "trt2"), cex = 0.8),
         points = list(pch = c(21), fill = c("black", "grey")),
         rep = FALSE)
       )
于 2012-01-13T17:27:53.383 回答
3

这可能是微不足道的，但您可以尝试：

xyplot(... , type=c("p","l","r"))

" p"添加点" l"用虚线连接它们" r"通过您的数据拟合线性模型。type="r"单独将仅绘制回归线而不显示数据点。

于 2013-01-31T10:25:24.870 回答
2

panel.lmline仅对原始数据使用该函数可能更容易：

xyplot(var ~ temp | subj,
        data = orig,
        panel = function(x,y,...,subscripts){
            fill <- my.fill[orig$trt[subscripts]]
            panel.xyplot(x, y, pch = 21, fill = my.fill,col = "black")
            panel.lmline(x,y,col = "salmon")
        },
        key = list(space = "right",
                     text = list(c("trt1", "trt2"), cex = 0.8),
                     points = list(pch = c(21), fill = c("black", "grey")),
                     rep = FALSE)
)

在此处输入图像描述

于 2012-01-13T17:19:02.873 回答