我在数据中有以下数据。称为t的帧
DayNum MeanVolume StdDev StdErr
1 13 207.0500 41.00045 5.125057
2 15 142.7625 27.87236 3.484045
3 18 77.5500 19.43928 2.429910
4 21 66.3750 20.56403 2.570504
5 26 67.0500 29.01576 3.626970
6 29 66.4750 25.94537 3.243171
7 33 76.9625 25.31374 3.164218
8 36 91.2875 37.01719 4.627149
9 40 102.0500 29.39898 3.674872
10 43 100.8250 24.22830 3.028538
11 47 120.5125 28.80592 3.600740
12 50 147.8875 35.82894 4.478617
13 54 126.7875 45.43204 5.679004
14 57 139.8500 56.01117 7.001397
15 60 179.1375 69.64526 8.705658
16 64 149.7625 39.10265 4.887831
17 68 229.5250 121.08411 15.135514
18 71 236.5125 76.23146 9.528933
19 75 243.2750 101.69474 12.711842
20 78 331.6750 141.25344 17.656680
21 82 348.2875 122.86359 15.357948
22 85 353.7750 187.24641 23.405801
23 89 385.4000 154.05826 19.257283
24 92 500.9875 263.43714 32.929642
25 95 570.2250 301.82686 37.728358
26 98 692.2250 344.71226 43.089032
27 102 692.8000 283.94120 35.492650
28 105 759.2000 399.19323 49.899153
29 109 898.2375 444.94289 55.617861
30 112 920.1000 515.79597 64.474496
我试图拟合t中的x=DayNum到y=MeanVolume。
以下是我所做的:
适合数据
model<-lm(log(t$MeanVolume) ~ t$DayNum, data=t)
绘图数据
plot(MeanVolume~DayNum, data=t, ylab="Mean Volume (mm3)", xlim=c(0,120), ylim=c(0,1000))
arrows(t$DayNum, t$MeanVolume-t$StdErr, t$DayNum, t$MeanVolume+t$StdErr, length=0.01, angle=90, code=3)
创建拟合数据
t$pred<-exp(predict(model))
绘图拟合
lines(t$DayNum,t$pred,col="blue")
另一方面,如果我使用ggplot2通过使用
ggplot(data = t, mapping = aes(x = DayNum, y=MeanVolume)) +
geom_line() +
geom_point(size=3, color="blue") +
geom_smooth(method="glm", method.args=list(family=gaussian(link="log"))) +
labs(x="Days", y="Mean Volume (mm3)", title="Data") +
geom_errorbar(aes(ymin = MeanVolume - StdErr, ymax = MeanVolume + StdErr), width=.2)
如您所见,ggplot情况下的拟合曲线优于plot情况下的拟合曲线。为什么?此外,我想拟合参数,如截距和指数拟合线的斜率。如何从ggplot调用中提取它们?
