如何以 "graphpad prism" 风格绘制三个不同组随时间的变化？
How to plot variation over time in three different groups, in a "graphpad prism" style?
我有这个数据集：
structure(list(time = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14, 15), ttt1_1 = c(0, 15, 20, 30, 40, 50, 60, 70, 80, 90,
130, 160, 240, 320, 450), ttt1_2 = c(0, 17, 22, 34, 50, 50, 65,
75, 90, 120, 160, 200, 300, 400, 500), ttt1_3 = c(0, 19, 25,
36, 47, 60, 70, 86, 110, 130, 195, 240, 360, 480, 650), ttt2_1 = c(0,
45, 60, 90, 120, 150, 210, 245, 280, 315, 455, 560, 720, 960,
1350), ttt2_2 = c(0, 51, 66, 102, 130, 150, 228, 262, 315, 420,
560, 700, 900, 1200, 1500), ttt2_3 = c(0, 57, 75, 108, 141, 180,
245, 301, 385, 455, 683, 840, 1080, 1440, 1950), ttt3_1 = c(0,
90, 120, 180, 240, 300, 420, 490, 560, 630, 910, 1120, 1440,
1920, 2700), ttt3_2 = c(0, 102, 132, 204, 300, 300, 455, 525,
630, 840, 1120, 1400, 1800, 2400, 3000), ttt3_3 = c(0, 114, 150,
216, 282, 360, 490, 602, 770, 910, 1365, 1680, 2160, 2880, 3900
)), row.names = c(NA, 15L), class = "data.frame")
看起来像这样：
> datapoids
time ttt1_1 ttt1_2 ttt1_3 ttt2_1 ttt2_2 ttt2_3 ttt3_1 ttt3_2 ttt3_3
1 1 0 0 0 0 0 0 0 0 0
2 2 15 17 19 45 51 57 90 102 114
3 3 20 22 25 60 66 75 120 132 150
4 4 30 34 36 90 102 108 180 204 216
5 5 40 50 47 120 130 141 240 300 282
6 6 50 50 60 150 150 180 300 300 360
7 7 60 65 70 210 228 245 420 455 490
8 8 70 75 86 245 262 301 490 525 602
9 9 80 90 110 280 315 385 560 630 770
10 10 90 120 130 315 420 455 630 840 910
11 11 130 160 195 455 560 683 910 1120 1365
12 12 160 200 240 560 700 840 1120 1400 1680
13 13 240 300 360 720 900 1080 1440 1800 2160
14 14 320 400 480 960 1200 1440 1920 2400 2880
15 15 450 500 650 1350 1500 1950 2700 3000 3900
此数据集表示 9 个人（3 个不同组中的 3 个人：ttt1、ttt2、ttt3）的体重随时间的变化（第一列 = 经过的时间，以天为单位）。
首先，我试图绘制这种图形（使用 Graphpad Prism 完成）：
但到目前为止，我唯一设法得到的是这个（我一次只能绘制一列，我想绘制 3 列的平均值（ttt1_1，ttt1_2, ttt1_3 例如），并为我的三个组（ttt1、ttt2、ttt3）执行此操作。
ggplot(data=datapoids, aes(x=time,y=ttt3_1)) +
geom_point(size=2)
哪个给我：
plot with ggplot2
知道如何用 ggplot2 得到我在 GraphPad 上得到的东西吗？
任何类型的建议都会有很大帮助！
更新 1
我将数据框的组织方式更改为如下所示：
> dput(head(datapoids, 60))
structure(list(time = c(1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5,
5, 5, 6, 6, 6, 7, 7, 7, 8, 8, 8, 9, 9, 9, 10, 10, 10, 11, 11,
11, 12, 12, 12, 13, 13, 13, 14, 14, 14, 15, 15, 15), group = c(1,
2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1,
2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1,
2, 3), m1 = c(0, 0, 0, 15, 45, 90, 20, 60, 120, 30, 90, 180,
40, 120, 240, 50, 150, 300, 60, 210, 420, 70, 245, 490, 80, 280,
560, 90, 315, 630, 130, 455, 910, 160, 560, 1120, 240, 720, 1440,
320, 960, 1920, 450, 1350, 2700), m2 = c(0, 0, 0, 17, 51, 102,
22, 66, 132, 34, 102, 204, 50, 130, 300, 50, 150, 300, 65, 228,
455, 75, 262, 525, 90, 315, 630, 120, 420, 840, 160, 560, 1120,
200, 700, 1400, 300, 900, 1800, 400, 1200, 2400, 500, 1500, 3000
), m3 = c(0, 0, 0, 19, 57, 114, 25, 75, 150, 36, 108, 216, 47,
141, 282, 60, 180, 360, 70, 245, 490, 86, 301, 602, 110, 385,
770, 130, 455, 910, 195, 683, 1365, 240, 840, 1680, 360, 1080,
2160, 480, 1440, 2880, 650, 1950, 3900)), row.names = c(NA, -45L
), class = c("tbl_df", "tbl", "data.frame"))
> datapoids
# A tibble: 45 x 5
time group m1 m2 m3
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1 0 0 0
2 1 2 0 0 0
3 1 3 0 0 0
4 2 1 15 17 19
5 2 2 45 51 57
6 2 3 90 102 114
7 3 1 20 22 25
8 3 2 60 66 75
9 3 3 120 132 150
10 4 1 30 34 36
# ... with 35 more rows
第1列代表经过的时间，第2列是小组，第3-4-5列是每组三个人。
到目前为止，我设法在图表上获得了三组数据，但每次只有 1 个人，我无法获得平均值 +/- SD...
ggplot(datapoids, aes(x = time, y = m1, group = group)) +
geom_point()
three groups but only one individual per group
更新 2
好的，这是另一个更新。
我已将我的数据集格式化为如下所示：
> print.data.frame(datapoids)
weight group time
1 0 1 1
2 0 1 1
3 0 1 1
4 0 2 1
5 0 2 1
6 0 2 1
7 0 3 1
8 0 3 1
9 0 3 1
10 15 1 2
11 17 1 2
12 19 1 2
13 45 2 2
14 51 2 2
15 57 2 2
16 90 3 2
17 102 3 2
18 114 3 2
19 20 1 3
20 22 1 3
21 25 1 3
22 60 2 3
23 66 2 3
24 75 2 3
25 120 3 3
26 132 3 3
27 150 3 3
28 30 1 4
29 34 1 4
30 36 1 4
31 90 2 4
32 102 2 4
33 108 2 4
34 180 3 4
35 204 3 4
36 216 3 4
37 40 1 5
38 50 1 5
39 47 1 5
40 120 2 5
41 130 2 5
42 141 2 5
43 240 3 5
44 300 3 5
45 282 3 5
46 50 1 6
47 50 1 6
48 60 1 6
49 150 2 6
50 150 2 6
51 180 2 6
52 300 3 6
53 300 3 6
54 360 3 6
55 60 1 7
56 65 1 7
57 70 1 7
58 210 2 7
59 228 2 7
60 245 2 7
61 420 3 7
62 455 3 7
63 490 3 7
64 70 1 8
65 75 1 8
66 86 1 8
67 245 2 8
68 262 2 8
69 301 2 8
70 490 3 8
71 525 3 8
72 602 3 8
73 80 1 9
74 90 1 9
75 110 1 9
76 280 2 9
77 315 2 9
78 385 2 9
79 560 3 9
80 630 3 9
81 770 3 9
82 90 1 10
83 120 1 10
84 130 1 10
85 315 2 10
86 420 2 10
87 455 2 10
88 630 3 10
89 840 3 10
90 910 3 10
91 130 1 11
92 160 1 11
93 195 1 11
94 455 2 11
95 560 2 11
96 683 2 11
97 910 3 11
98 1120 3 11
99 1365 3 11
100 160 1 12
101 200 1 12
102 240 1 12
103 560 2 12
104 700 2 12
105 840 2 12
106 1120 3 12
107 1400 3 12
108 1680 3 12
109 240 1 13
110 300 1 13
111 360 1 13
112 720 2 13
113 900 2 13
114 1080 2 13
115 1440 3 13
116 1800 3 13
117 2160 3 13
118 320 1 14
119 400 1 14
120 480 1 14
121 960 2 14
122 1200 2 14
123 1440 2 14
124 1920 3 14
125 2400 3 14
126 2880 3 14
127 450 1 15
128 500 1 15
129 650 1 15
130 1350 2 15
131 1500 2 15
132 1950 2 15
133 2700 3 15
134 3000 3 15
135 3900 3 15
> dput(head(datapoids, 10000000))
structure(list(weight = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 15, 17,
19, 45, 51, 57, 90, 102, 114, 20, 22, 25, 60, 66, 75, 120, 132,
150, 30, 34, 36, 90, 102, 108, 180, 204, 216, 40, 50, 47, 120,
130, 141, 240, 300, 282, 50, 50, 60, 150, 150, 180, 300, 300,
360, 60, 65, 70, 210, 228, 245, 420, 455, 490, 70, 75, 86, 245,
262, 301, 490, 525, 602, 80, 90, 110, 280, 315, 385, 560, 630,
770, 90, 120, 130, 315, 420, 455, 630, 840, 910, 130, 160, 195,
455, 560, 683, 910, 1120, 1365, 160, 200, 240, 560, 700, 840,
1120, 1400, 1680, 240, 300, 360, 720, 900, 1080, 1440, 1800,
2160, 320, 400, 480, 960, 1200, 1440, 1920, 2400, 2880, 450,
500, 650, 1350, 1500, 1950, 2700, 3000, 3900), group = structure(c(1L,
1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L,
3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L,
3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L,
2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L,
1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L,
1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L,
3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L,
2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L), .Label = c("1", "2", "3"), class = "factor"),
time = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L), .Label = c("1", "2", "3", "4", "5", "6",
"7", "8", "9", "10", "11", "12", "13", "14", "15"), class = "factor")), row.names = c(NA,
-135L), class = c("tbl_df", "tbl", "data.frame"))
还有这个：
ggplot(datapoids, aes(x = time, y = weight)) +
geom_boxplot(aes(fill=group), position="identity") +
geom_point()
我设法得到了这个（这还不是意味着 +/- SD）：
我的方程式搜索结果显示 S 型磁饱和方程式最适合三组的平均重量。根据您的评论，初始运行时间为零用于首次测量。
体重 = a * x * (1.0 + b * exp(c * ElapsedDays))
我对各组的拟合结果如下：
对于第 1 组，参数为：
a = 8.2069429067318076E+00
b = 3.4803188790045243E-02
c = 3.3342423727900505E-01
R-squared = 0.997 和 RMSE = 7.96
对于第 2 组，参数为：
a = 2.7849455860678560E+01
b = 4.7404034036124171E-02
c = 2.9879802065164002E-01
R-squared = 0.999 和 RMSE = 12.85
对于第 3 组，参数为：
a = 5.6346090778919425E+01
b = 4.5307611859092961E-02
c = 3.0096010027034203E-01
R-squared = 0.999 和 RMSE = 25.941
感谢@Axeman，我找到了答案：
ggplot(datapoids, aes(x = time, y = weight)) +
stat_summary(aes(color = group), fun.data="mean_sdl", fun.args = list(mult=1), geom="pointrange", position = "identity")
其中 fun.data="mean_sdl" 显示平均值 +/- 常数时间标准差，fun.args = list(mult=1) 定义常数（此处 = 1）。
我终于得到了我想要的
我只需要找到方法（进行中）：
- 改变每组的颜色
- 为每个组更改符号
- 在误差条上方和下方添加一个小条
更新
这是最终结果：
有：
ggplot(datapoidsmono, aes(x = time, y = weight)) +
stat_summary(aes(color = group), fun.data="mean_sdl", fun.args = list(mult=1), geom="errorbar", position = "identity", size=0.5, width=0.2, show.legend = T) +
stat_summary(fun.y = "mean", geom = "point", size=3, aes(shape=group,colour=group)) +
scale_x_discrete(name = "Days after injection") +
scale_y_continuous(name = "Weight (g)", limits=c(0, 4000), breaks = seq(0, 4000,500)) +
theme(axis.line.x = element_line(size = 0.5, colour = "black"),axis.text.x = element_text(colour="black", size = 12),axis.line.y = element_line(size = 0.5, colour = "black"),axis.text.y = element_text(colour="black", size = 12),axis.title = element_text(size =15, face="bold"),plot.title = element_text(size =20, face = "bold"),panel.grid.major = element_line(colour = "#F1F1F1"),panel.grid.minor = element_blank(), panel.background = element_blank()) +
scale_color_manual(values=c("green", "blue", "red")) +
ggtitle("Weight variation over time") + theme(plot.title = element_text(hjust = 0.5))
我有这个数据集：
structure(list(time = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14, 15), ttt1_1 = c(0, 15, 20, 30, 40, 50, 60, 70, 80, 90,
130, 160, 240, 320, 450), ttt1_2 = c(0, 17, 22, 34, 50, 50, 65,
75, 90, 120, 160, 200, 300, 400, 500), ttt1_3 = c(0, 19, 25,
36, 47, 60, 70, 86, 110, 130, 195, 240, 360, 480, 650), ttt2_1 = c(0,
45, 60, 90, 120, 150, 210, 245, 280, 315, 455, 560, 720, 960,
1350), ttt2_2 = c(0, 51, 66, 102, 130, 150, 228, 262, 315, 420,
560, 700, 900, 1200, 1500), ttt2_3 = c(0, 57, 75, 108, 141, 180,
245, 301, 385, 455, 683, 840, 1080, 1440, 1950), ttt3_1 = c(0,
90, 120, 180, 240, 300, 420, 490, 560, 630, 910, 1120, 1440,
1920, 2700), ttt3_2 = c(0, 102, 132, 204, 300, 300, 455, 525,
630, 840, 1120, 1400, 1800, 2400, 3000), ttt3_3 = c(0, 114, 150,
216, 282, 360, 490, 602, 770, 910, 1365, 1680, 2160, 2880, 3900
)), row.names = c(NA, 15L), class = "data.frame")
看起来像这样：
> datapoids
time ttt1_1 ttt1_2 ttt1_3 ttt2_1 ttt2_2 ttt2_3 ttt3_1 ttt3_2 ttt3_3
1 1 0 0 0 0 0 0 0 0 0
2 2 15 17 19 45 51 57 90 102 114
3 3 20 22 25 60 66 75 120 132 150
4 4 30 34 36 90 102 108 180 204 216
5 5 40 50 47 120 130 141 240 300 282
6 6 50 50 60 150 150 180 300 300 360
7 7 60 65 70 210 228 245 420 455 490
8 8 70 75 86 245 262 301 490 525 602
9 9 80 90 110 280 315 385 560 630 770
10 10 90 120 130 315 420 455 630 840 910
11 11 130 160 195 455 560 683 910 1120 1365
12 12 160 200 240 560 700 840 1120 1400 1680
13 13 240 300 360 720 900 1080 1440 1800 2160
14 14 320 400 480 960 1200 1440 1920 2400 2880
15 15 450 500 650 1350 1500 1950 2700 3000 3900
此数据集表示 9 个人（3 个不同组中的 3 个人：ttt1、ttt2、ttt3）的体重随时间的变化（第一列 = 经过的时间，以天为单位）。
首先，我试图绘制这种图形（使用 Graphpad Prism 完成）：
但到目前为止，我唯一设法得到的是这个（我一次只能绘制一列，我想绘制 3 列的平均值（ttt1_1，ttt1_2, ttt1_3 例如），并为我的三个组（ttt1、ttt2、ttt3）执行此操作。
ggplot(data=datapoids, aes(x=time,y=ttt3_1)) +
geom_point(size=2)
哪个给我： plot with ggplot2
知道如何用 ggplot2 得到我在 GraphPad 上得到的东西吗？ 任何类型的建议都会有很大帮助！
更新 1
我将数据框的组织方式更改为如下所示：
> dput(head(datapoids, 60))
structure(list(time = c(1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5,
5, 5, 6, 6, 6, 7, 7, 7, 8, 8, 8, 9, 9, 9, 10, 10, 10, 11, 11,
11, 12, 12, 12, 13, 13, 13, 14, 14, 14, 15, 15, 15), group = c(1,
2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1,
2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1,
2, 3), m1 = c(0, 0, 0, 15, 45, 90, 20, 60, 120, 30, 90, 180,
40, 120, 240, 50, 150, 300, 60, 210, 420, 70, 245, 490, 80, 280,
560, 90, 315, 630, 130, 455, 910, 160, 560, 1120, 240, 720, 1440,
320, 960, 1920, 450, 1350, 2700), m2 = c(0, 0, 0, 17, 51, 102,
22, 66, 132, 34, 102, 204, 50, 130, 300, 50, 150, 300, 65, 228,
455, 75, 262, 525, 90, 315, 630, 120, 420, 840, 160, 560, 1120,
200, 700, 1400, 300, 900, 1800, 400, 1200, 2400, 500, 1500, 3000
), m3 = c(0, 0, 0, 19, 57, 114, 25, 75, 150, 36, 108, 216, 47,
141, 282, 60, 180, 360, 70, 245, 490, 86, 301, 602, 110, 385,
770, 130, 455, 910, 195, 683, 1365, 240, 840, 1680, 360, 1080,
2160, 480, 1440, 2880, 650, 1950, 3900)), row.names = c(NA, -45L
), class = c("tbl_df", "tbl", "data.frame"))
> datapoids
# A tibble: 45 x 5
time group m1 m2 m3
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1 0 0 0
2 1 2 0 0 0
3 1 3 0 0 0
4 2 1 15 17 19
5 2 2 45 51 57
6 2 3 90 102 114
7 3 1 20 22 25
8 3 2 60 66 75
9 3 3 120 132 150
10 4 1 30 34 36
# ... with 35 more rows
第1列代表经过的时间，第2列是小组，第3-4-5列是每组三个人。
到目前为止，我设法在图表上获得了三组数据，但每次只有 1 个人，我无法获得平均值 +/- SD...
ggplot(datapoids, aes(x = time, y = m1, group = group)) +
geom_point()
three groups but only one individual per group
更新 2
好的，这是另一个更新。 我已将我的数据集格式化为如下所示：
> print.data.frame(datapoids)
weight group time
1 0 1 1
2 0 1 1
3 0 1 1
4 0 2 1
5 0 2 1
6 0 2 1
7 0 3 1
8 0 3 1
9 0 3 1
10 15 1 2
11 17 1 2
12 19 1 2
13 45 2 2
14 51 2 2
15 57 2 2
16 90 3 2
17 102 3 2
18 114 3 2
19 20 1 3
20 22 1 3
21 25 1 3
22 60 2 3
23 66 2 3
24 75 2 3
25 120 3 3
26 132 3 3
27 150 3 3
28 30 1 4
29 34 1 4
30 36 1 4
31 90 2 4
32 102 2 4
33 108 2 4
34 180 3 4
35 204 3 4
36 216 3 4
37 40 1 5
38 50 1 5
39 47 1 5
40 120 2 5
41 130 2 5
42 141 2 5
43 240 3 5
44 300 3 5
45 282 3 5
46 50 1 6
47 50 1 6
48 60 1 6
49 150 2 6
50 150 2 6
51 180 2 6
52 300 3 6
53 300 3 6
54 360 3 6
55 60 1 7
56 65 1 7
57 70 1 7
58 210 2 7
59 228 2 7
60 245 2 7
61 420 3 7
62 455 3 7
63 490 3 7
64 70 1 8
65 75 1 8
66 86 1 8
67 245 2 8
68 262 2 8
69 301 2 8
70 490 3 8
71 525 3 8
72 602 3 8
73 80 1 9
74 90 1 9
75 110 1 9
76 280 2 9
77 315 2 9
78 385 2 9
79 560 3 9
80 630 3 9
81 770 3 9
82 90 1 10
83 120 1 10
84 130 1 10
85 315 2 10
86 420 2 10
87 455 2 10
88 630 3 10
89 840 3 10
90 910 3 10
91 130 1 11
92 160 1 11
93 195 1 11
94 455 2 11
95 560 2 11
96 683 2 11
97 910 3 11
98 1120 3 11
99 1365 3 11
100 160 1 12
101 200 1 12
102 240 1 12
103 560 2 12
104 700 2 12
105 840 2 12
106 1120 3 12
107 1400 3 12
108 1680 3 12
109 240 1 13
110 300 1 13
111 360 1 13
112 720 2 13
113 900 2 13
114 1080 2 13
115 1440 3 13
116 1800 3 13
117 2160 3 13
118 320 1 14
119 400 1 14
120 480 1 14
121 960 2 14
122 1200 2 14
123 1440 2 14
124 1920 3 14
125 2400 3 14
126 2880 3 14
127 450 1 15
128 500 1 15
129 650 1 15
130 1350 2 15
131 1500 2 15
132 1950 2 15
133 2700 3 15
134 3000 3 15
135 3900 3 15
> dput(head(datapoids, 10000000))
structure(list(weight = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 15, 17,
19, 45, 51, 57, 90, 102, 114, 20, 22, 25, 60, 66, 75, 120, 132,
150, 30, 34, 36, 90, 102, 108, 180, 204, 216, 40, 50, 47, 120,
130, 141, 240, 300, 282, 50, 50, 60, 150, 150, 180, 300, 300,
360, 60, 65, 70, 210, 228, 245, 420, 455, 490, 70, 75, 86, 245,
262, 301, 490, 525, 602, 80, 90, 110, 280, 315, 385, 560, 630,
770, 90, 120, 130, 315, 420, 455, 630, 840, 910, 130, 160, 195,
455, 560, 683, 910, 1120, 1365, 160, 200, 240, 560, 700, 840,
1120, 1400, 1680, 240, 300, 360, 720, 900, 1080, 1440, 1800,
2160, 320, 400, 480, 960, 1200, 1440, 1920, 2400, 2880, 450,
500, 650, 1350, 1500, 1950, 2700, 3000, 3900), group = structure(c(1L,
1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L,
3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L,
3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L,
2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L,
1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L,
1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L,
3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L,
2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L), .Label = c("1", "2", "3"), class = "factor"),
time = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L), .Label = c("1", "2", "3", "4", "5", "6",
"7", "8", "9", "10", "11", "12", "13", "14", "15"), class = "factor")), row.names = c(NA,
-135L), class = c("tbl_df", "tbl", "data.frame"))
还有这个：
ggplot(datapoids, aes(x = time, y = weight)) +
geom_boxplot(aes(fill=group), position="identity") +
geom_point()
我设法得到了这个（这还不是意味着 +/- SD）：
我的方程式搜索结果显示 S 型磁饱和方程式最适合三组的平均重量。根据您的评论，初始运行时间为零用于首次测量。
体重 = a * x * (1.0 + b * exp(c * ElapsedDays))
我对各组的拟合结果如下：
对于第 1 组，参数为：
a = 8.2069429067318076E+00
b = 3.4803188790045243E-02
c = 3.3342423727900505E-01
R-squared = 0.997 和 RMSE = 7.96
对于第 2 组，参数为：
a = 2.7849455860678560E+01
b = 4.7404034036124171E-02
c = 2.9879802065164002E-01
R-squared = 0.999 和 RMSE = 12.85
对于第 3 组，参数为：
a = 5.6346090778919425E+01
b = 4.5307611859092961E-02
c = 3.0096010027034203E-01
R-squared = 0.999 和 RMSE = 25.941
感谢@Axeman，我找到了答案：
ggplot(datapoids, aes(x = time, y = weight)) +
stat_summary(aes(color = group), fun.data="mean_sdl", fun.args = list(mult=1), geom="pointrange", position = "identity")
其中 fun.data="mean_sdl" 显示平均值 +/- 常数时间标准差，fun.args = list(mult=1) 定义常数（此处 = 1）。
我终于得到了我想要的
我只需要找到方法（进行中）：
- 改变每组的颜色
- 为每个组更改符号
- 在误差条上方和下方添加一个小条
更新
这是最终结果：
有：
ggplot(datapoidsmono, aes(x = time, y = weight)) +
stat_summary(aes(color = group), fun.data="mean_sdl", fun.args = list(mult=1), geom="errorbar", position = "identity", size=0.5, width=0.2, show.legend = T) +
stat_summary(fun.y = "mean", geom = "point", size=3, aes(shape=group,colour=group)) +
scale_x_discrete(name = "Days after injection") +
scale_y_continuous(name = "Weight (g)", limits=c(0, 4000), breaks = seq(0, 4000,500)) +
theme(axis.line.x = element_line(size = 0.5, colour = "black"),axis.text.x = element_text(colour="black", size = 12),axis.line.y = element_line(size = 0.5, colour = "black"),axis.text.y = element_text(colour="black", size = 12),axis.title = element_text(size =15, face="bold"),plot.title = element_text(size =20, face = "bold"),panel.grid.major = element_line(colour = "#F1F1F1"),panel.grid.minor = element_blank(), panel.background = element_blank()) +
scale_color_manual(values=c("green", "blue", "red")) +
ggtitle("Weight variation over time") + theme(plot.title = element_text(hjust = 0.5))