使用错误栏在xyplot中绘制SE

时间：2016-10-23 17:28:17

标签： r lattice standard-error

我正在尝试构建一个简单的XY图表，其中包含两组不同奶牛的产奶量（称为FCM）（来自混合模型的输出，使用lsmeans和SE）。 我能够使用格子中的xyplot函数构建显示lsmeans的图：

library(lattice)    
xyplot(lsmean~Time, type="b", group=Group, data=lsmeans2[order(lsmeans2$Time),],
       pch=16, ylim=c(10,35), col=c("darkorange","darkgreen"), 
       ylab="FCM (kg/day)", xlab="Week", lwd=2, 
       key=list(space="top",
                lines=list(col=c("darkorange","darkgreen"),lty=c(1,1),lwd=2),
                text=list(c("Confinement Group","Pasture Group"), cex=0.8)))

我现在想添加错误栏。我尝试了一些panel.arrow函数，只是从其他例子中复制和粘贴但没有进一步。

我真的很感激一些帮助！

我的lsmeans2数据集：

Group Time lsmean SE df lower.CL upper.CL
Stall wk1  26.23299 0.6460481 59 24.19243 28.27356
Weide wk1  25.12652 0.6701080 58 23.00834 27.24471
Stall wk10 21.89950 0.6460589 59 19.85890 23.94010
Weide wk10 18.45845 0.6679617 58 16.34705 20.56986
Stall wk2  25.38004 0.6460168 59 23.33957 27.42050
Weide wk2  22.90409 0.6679617 58 20.79269 25.01549
Stall wk3  25.02474 0.6459262 59 22.98455 27.06492
Weide wk3  24.05886 0.6679436 58 21.94751 26.17020
Stall wk4  23.91630 0.6456643 59 21.87694 25.95565
Weide wk4  22.23608 0.6678912 58 20.12490 24.34726
Stall wk5  23.97382 0.6493483 59 21.92283 26.02481
Weide wk5  18.14550 0.6677398 58 16.03480 20.25620
Stall wk6  24.48899 0.6456643 59 22.44963 26.52834
Weide wk6  19.40022 0.6697394 58 17.28319 21.51724
Stall wk7  24.98107 0.6459262 59 22.94089 27.02126
Weide wk7  19.71200 0.6677398 58 17.60129 21.82270
Stall wk8  22.65167 0.6460168 59 20.61120 24.69214
Weide wk8  19.35759 0.6678912 58 17.24641 21.46877
Stall wk9  22.64381 0.6460481 59 20.60324 24.68438
Weide wk9  19.26869 0.6679436 58 17.15735 21.38004

2 个答案:

答案 0 :(得分：1)

您是否有特殊原因要使用xplot？ ggplot2更容易使用和更漂亮。这是我认为你想要的一个例子。

#load ggplot2
library(ggplot2)

#load data
d = structure(list(Group = structure(c(1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L), .Label = c("Stall", 
"Weide"), class = "factor"), Time = structure(c(1L, 1L, 2L, 2L, 
3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L, 7L, 8L, 8L, 9L, 9L, 10L, 
10L), .Label = c("wk1", "wk10", "wk2", "wk3", "wk4", "wk5", "wk6", 
"wk7", "wk8", "wk9"), class = "factor"), lsmean = c(26.23299, 
25.12652, 21.8995, 18.45845, 25.38004, 22.90409, 25.02474, 24.05886, 
23.9163, 22.23608, 23.97382, 18.1455, 24.48899, 19.40022, 24.98107, 
19.712, 22.65167, 19.35759, 22.64381, 19.26869), SE = c(0.6460481, 
0.670108, 0.6460589, 0.6679617, 0.6460168, 0.6679617, 0.6459262, 
0.6679436, 0.6456643, 0.6678912, 0.6493483, 0.6677398, 0.6456643, 
0.6697394, 0.6459262, 0.6677398, 0.6460168, 0.6678912, 0.6460481, 
0.6679436), df = c(59L, 58L, 59L, 58L, 59L, 58L, 59L, 58L, 59L, 
58L, 59L, 58L, 59L, 58L, 59L, 58L, 59L, 58L, 59L, 58L), lower.CL = c(24.19243, 
23.00834, 19.8589, 16.34705, 23.33957, 20.79269, 22.98455, 21.94751, 
21.87694, 20.1249, 21.92283, 16.0348, 22.44963, 17.28319, 22.94089, 
17.60129, 20.6112, 17.24641, 20.60324, 17.15735), upper.CL = c(28.27356, 
27.24471, 23.9401, 20.56986, 27.4205, 25.01549, 27.06492, 26.1702, 
25.95565, 24.34726, 26.02481, 20.2562, 26.52834, 21.51724, 27.02126, 
21.8227, 24.69214, 21.46877, 24.68438, 21.38004)), .Names = c("Group", 
"Time", "lsmean", "SE", "df", "lower.CL", "upper.CL"), class = "data.frame", row.names = c(NA, 
-20L))

#fix week
library(stringr)
library(magrittr)
d$Time %<>% as.character() %>% str_replace(pattern = "wk", replacement = "") %>% as.numeric()

#plot
ggplot(d, aes(Time, lsmean, color = Group, group = Group)) +
  geom_point() +
  geom_errorbar(aes(ymin = lower.CL, ymax = upper.CL), width = .2) +
  geom_line() +
  ylim(10, 35) +
  scale_x_continuous(name = "Week", breaks = 1:10) +
  ylab("FCM (kg/day)") +
  scale_color_discrete(label = c("Confinement Group","Pasture Group"))

enter image description here

答案 1 :(得分：1)

为了完整起见，这是使用xyplot的解决方案：

# Reproducible data
lsmeans2 = structure(list(Group = structure(c(1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L), .Label = c("Stall", 
"Weide"), class = "factor"), Time = structure(c(1L, 1L, 2L, 2L, 
3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L, 7L, 8L, 8L, 9L, 9L, 10L, 
10L), .Label = c("wk1", "wk10", "wk2", "wk3", "wk4", "wk5", "wk6", 
"wk7", "wk8", "wk9"), class = "factor"), lsmean = c(26.23299, 
25.12652, 21.8995, 18.45845, 25.38004, 22.90409, 25.02474, 24.05886, 
23.9163, 22.23608, 23.97382, 18.1455, 24.48899, 19.40022, 24.98107, 
19.712, 22.65167, 19.35759, 22.64381, 19.26869), SE = c(0.6460481, 
0.670108, 0.6460589, 0.6679617, 0.6460168, 0.6679617, 0.6459262, 
0.6679436, 0.6456643, 0.6678912, 0.6493483, 0.6677398, 0.6456643, 
0.6697394, 0.6459262, 0.6677398, 0.6460168, 0.6678912, 0.6460481, 
0.6679436), df = c(59L, 58L, 59L, 58L, 59L, 58L, 59L, 58L, 59L, 
58L, 59L, 58L, 59L, 58L, 59L, 58L, 59L, 58L, 59L, 58L), lower.CL = c(24.19243, 
23.00834, 19.8589, 16.34705, 23.33957, 20.79269, 22.98455, 21.94751, 
21.87694, 20.1249, 21.92283, 16.0348, 22.44963, 17.28319, 22.94089, 
17.60129, 20.6112, 17.24641, 20.60324, 17.15735), upper.CL = c(28.27356, 
27.24471, 23.9401, 20.56986, 27.4205, 25.01549, 27.06492, 26.1702, 
25.95565, 24.34726, 26.02481, 20.2562, 26.52834, 21.51724, 27.02126, 
21.8227, 24.69214, 21.46877, 24.68438, 21.38004)), .Names = c("Group", 
"Time", "lsmean", "SE", "df", "lower.CL", "upper.CL"), class = "data.frame", row.names = c(NA, 
-20L))

xyplot(lsmean~Time, type="b", group=Group, data=lsmeans2[order(lsmeans2$Time),],
       panel = function(x, y, ...){
         panel.arrows(x, y, x, lsmeans2$upper.CL, length = 0.15,
                      angle = 90, col=c("darkorange","darkgreen"))
         panel.arrows(x, y, x, lsmeans2$lower.CL, length = 0.15,
                      angle = 90, col=c("darkorange","darkgreen"))
         panel.xyplot(x,y, ...)
       },
       pch=16, ylim=c(10,35), col=c("darkorange","darkgreen"), 
       ylab="FCM (kg/day)", xlab="Week", lwd=2, 
       key=list(space="top",
                lines=list(col=c("darkorange","darkgreen"),lty=c(1,1),lwd=2),
                text=list(c("Confinement Group","Pasture Group"), cex=0.8)))

panel.arrows中的长度参数会更改错误头的宽度。你可以摆弄这个参数来获得你喜欢的宽度。

xyplot with wrong ordering

请注意，即使您在指定lsmeans2[order(lsmeans2$Time),]时有data =，但Time的顺序仍然是错误的。这是因为时间是一个因素，而R并不知道你希望它按wk的数字后缀排序。这意味着它会在wk2之前对wk10进行排序，因为1小于2.您可以使用下面的这个小技巧来正确排序：

# Order first by the character lenght, then by Time
Timelevels = levels(lsmeans2$Time) 
Timelevels = Timelevels[order(nchar(Timelevels), Timelevels)]

# Reorder the levels
lsmeans2$Time = factor(lsmeans2$Time, levels = Timelevels)

# Create Subset
lsmeansSub = lsmeans2[order(lsmeans2$Time),]

xyplot(lsmean~Time, type="b", group=Group, data=lsmeansSub,
       panel = function(x, y, yu, yl, ...){
         panel.arrows(x, y, x, lsmeansSub$upper.CL, length = 0.15,
                      angle = 90, col=c("darkorange","darkgreen"))
         panel.arrows(x, y, x, lsmeansSub$lower.CL, length = 0.15,
                      angle = 90, col=c("darkorange","darkgreen"))
         panel.xyplot(x, y, ...)
       },
       pch=16, ylim=c(10,35), col=c("darkorange","darkgreen"), 
       ylab="FCM (kg/day)", xlab="Week", lwd=2, 
       key=list(space="top",
                lines=list(col=c("darkorange","darkgreen"),lty=c(1,1),lwd=2),
                text=list(c("Confinement Group","Pasture Group"), cex=0.8)))

请注意，即使重新排序＆＃34; Time＆＃34;的级别，我仍然需要使用data =参数的排序数据。这是因为xyplot绘制了数据集中出现的顺序中的点，而不是因子级别的顺序。

xyplot with correct ordering

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