如何以“graphpad prism”风格在三个不同组中绘制随时间变化的曲线图？
我有以下数据集：如何以“graphpad prism”风格在三个不同组中绘制随时间变化的曲线图？,r,ggplot2,compare,curve-fitting,non-linear-regression,R,Ggplot2,Compare,Curve Fitting,Non Linear Regression,我有以下数据集： 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 =
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")
> 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
> 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"))
> 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
ggplot(data=datapoids, aes(x=time,y=ttt3_1)) +
geom_point(size=2)
ggplot(data=datapoids, aes(x=time,y=ttt3_1)) +
geom_point(size=2)
> 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
> 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"))
> 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
> 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
> 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"))
> 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
ggplot(datapoids, aes(x = time, y = weight)) +
geom_boxplot(aes(fill=group), position="identity") +
geom_point()
我的方程搜索结果显示，sigmoidal型磁饱和方程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第一次测量 重量=a*x*1.0+b*expc*过期天数 我对每组的拟合结果如下： 对于组1，参数为：
a = 8.2069429067318076E+00
b = 3.4803188790045243E-02
c = 3.3342423727900505E-01
a = 2.7849455860678560E+01
b = 4.7404034036124171E-02
c = 2.9879802065164002E-01
a = 5.6346090778919425E+01
b = 4.5307611859092961E-02
c = 3.0096010027034203E-01
a = 8.2069429067318076E+00
b = 3.4803188790045243E-02
c = 3.3342423727900505E-01
a = 2.7849455860678560E+01
b = 4.7404034036124171E-02
c = 2.9879802065164002E-01
a = 5.6346090778919425E+01
b = 4.5307611859092961E-02
c = 3.0096010027034203E-01
a = 8.2069429067318076E+00
b = 3.4803188790045243E-02
c = 3.3342423727900505E-01
a = 2.7849455860678560E+01
b = 4.7404034036124171E-02
c = 2.9879802065164002E-01
a = 5.6346090778919425E+01
b = 4.5307611859092961E-02
c = 3.0096010027034203E-01
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")
由于所有初始测量的权重均为零，那么第一时间是否为零天？也就是说，第一次测量是否在没有时间过去的情况下完成？是的，第一时间点是0天；是的，开始治疗前的第一次测量GgplotDatapoids，aesx=as.numerictime，y=weight，color=group+stat\u summaryfun.data=mean\u sdl，fun.args=listmultt=1。感谢您指出stat\u summary函数，我刚刚更改了color=group在stat\summary部分中的位置，它成功了！如果可以的话，我会把它贴在第一个帖子下面的答案中，然后为我的第二个问题创建另一个帖子，在各组之间绘制非线性回归图，并对它们进行比较。我会指出，对于b和c，所有参数都有相似的参数，而作为振幅的参数a对于所有三个组都是不同的。这是一个强烈的暗示，对于所有群体来说，时间和体重之间可能存在着一些简单的，现在不那么隐蔽的关系。谢谢@JamesPhillips。我已经生成了数据，以显示我想对未来的措施做些什么，但这些数据完全是随机的。。。所以我的问题更多的是关于如何绘制图和比较斜率，而不是关于数据本身。。。无论如何，再次感谢你@Axeman根据原始海报对这个答案的评论，是的。