R中的绘图与ggplot2以及如何提取拟合参数

我在一个名为t的data.frame中有以下数据

   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

我试图将t中的x=DayNum与y=MeanVolume进行拟合

以下是我所做的：

适合数据

model<-lm(log(t$MeanVolume) ~ t$DayNum, data=t)

创建拟合数据

t$pred<-exp(predict(model))

另一方面，如果我使用ggplot2通过

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)

我得到下面的情节

---

如您所见，ggplot情况下的拟合曲线比plot情况下的拟合曲线更好。为什么？我还想拟合参数，如截距和指数拟合线的斜率。如何从ggplot调用中提取它们

带有对数变换y的lm与带有高斯误差分布和对数链接的glm不同（至于为什么在@Lyngbakr的评论中检查链接）

要从ggplot中提取数据，可以使用：

build = ggplot_build(p)

曲线的数据位于

build$data[[3]]

p +  geom_line(data = build$data[[3]], aes(x = x, y = y), lty = 2, color = "red", size = 1.5)

---

此数据与

pred\u glm

中的数据相同-其密度稍高（数据点较多）。据我所知，没有从ggplot中提取系数的方法，只有预测，但您始终可以如上所述构建glm模型。

差异已得到解释。据我所知，您无法从

ggplot

中提取拟合系数，因此您应该单独拟合您的模型。您好，非常感谢。我从你的回答中学到了很多，但正如林巴克所说，我仍然无法从ggplot构建数据中获得拟合信息（如截距和斜率）。因此，我必须明确地适应模型，以提取它看起来的信息。

gz <- read.table("somet.txt")
gz <- as.data.frame(gz)
model_lm <- lm(log(MeanVolume) ~ DayNum, data = gz)
model_glm <- glm(MeanVolume ~ DayNum, data = gz, family = gaussian(link = "log"))
pred_lm <- exp(predict(model_lm))
pred_glm <- predict(model_glm, type = "response")

plot(MeanVolume ~ DayNum, data = gz, ylab = "Mean Volume (mm3)", xlim = c(0,120), ylim = c(0,1000))
arrows(gz$DayNum, gz$MeanVolume - gz$StdErr, gz$DayNum, gz$MeanVolume + gz$StdErr, length = 0.01, angle = 90, code = 3)

lines(gz$DayNum, pred_lm, col = "blue")
lines(gz$DayNum, pred_glm, col = "red")

legend("topleft", col = c("blue", "red"), lty = 1, legend = c("lm", "glm"))

library(ggplot2)
p = ggplot(data = gz, 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)

build = ggplot_build(p)

p +  geom_line(data = build$data[[3]], aes(x = x, y = y), lty = 2, color = "red", size = 1.5)