r - 提取 stat_smooth 线拟合的方法

Question

有没有办法提取从 stat_smooth 返回的拟合线的值？

我使用的代码如下所示:

p <- ggplot(df1, aes(x=Days, y= Qty,group=Category,color=Category))
p <- p + stat_smooth(method=glm, fullrange=TRUE)+ geom_point())

这位新的 r 用户将非常感谢任何指导。

Answer 1

模仿@James 的例子

p <- qplot(hp,wt,data=mtcars) + stat_smooth()

您可以使用 ggplot 构建过程的中间阶段来提取绘制的数据。 ggplot_build 的结果是一个列表，其中一个组件是 data，它是一个数据帧列表，其中包含要绘制的计算值。在这种情况下，列表是两个数据帧，因为原始 qplot 为点创建了一个数据帧，而 stat_smooth 创建了一个平滑数据帧。

> ggplot_build(p)$data[[2]]
geom_smooth: method="auto" and size of largest group is <1000, so using loess. Use 'method = x' to change the smoothing method.
           x        y     ymin     ymax        se PANEL group
1   52.00000 1.993594 1.149150 2.838038 0.4111133     1     1
2   55.58228 2.039986 1.303264 2.776709 0.3586695     1     1
3   59.16456 2.087067 1.443076 2.731058 0.3135236     1     1
4   62.74684 2.134889 1.567662 2.702115 0.2761514     1     1
5   66.32911 2.183533 1.677017 2.690049 0.2465948     1     1
6   69.91139 2.232867 1.771739 2.693995 0.2244980     1     1
7   73.49367 2.282897 1.853241 2.712552 0.2091756     1     1
8   77.07595 2.333626 1.923599 2.743652 0.1996193     1     1
9   80.65823 2.385059 1.985378 2.784740 0.1945828     1     1
10  84.24051 2.437200 2.041282 2.833117 0.1927505     1     1
11  87.82278 2.490053 2.093808 2.886297 0.1929096     1     1
12  91.40506 2.543622 2.145018 2.942225 0.1940582     1     1
13  94.98734 2.597911 2.196466 2.999355 0.1954412     1     1
14  98.56962 2.652852 2.249260 3.056444 0.1964867     1     1
15 102.15190 2.708104 2.303465 3.112744 0.1969967     1     1
16 105.73418 2.764156 2.357927 3.170385 0.1977705     1     1
17 109.31646 2.821771 2.414230 3.229311 0.1984091     1     1
18 112.89873 2.888224 2.478136 3.298312 0.1996493     1     1
19 116.48101 2.968745 2.531045 3.406444 0.2130917     1     1
20 120.06329 3.049545 2.552102 3.546987 0.2421773     1     1
21 123.64557 3.115893 2.573577 3.658208 0.2640235     1     1
22 127.22785 3.156368 2.601664 3.711072 0.2700548     1     1
23 130.81013 3.175495 2.625951 3.725039 0.2675429     1     1
24 134.39241 3.181411 2.645191 3.717631 0.2610560     1     1
25 137.97468 3.182252 2.658993 3.705511 0.2547460     1     1
26 141.55696 3.186155 2.670350 3.701961 0.2511175     1     1
27 145.13924 3.201258 2.687208 3.715308 0.2502626     1     1
28 148.72152 3.235698 2.721744 3.749652 0.2502159     1     1
29 152.30380 3.291766 2.782767 3.800765 0.2478037     1     1
30 155.88608 3.353259 2.857911 3.848607 0.2411575     1     1
31 159.46835 3.418409 2.938257 3.898561 0.2337596     1     1
32 163.05063 3.487074 3.017321 3.956828 0.2286972     1     1
33 166.63291 3.559111 3.092367 4.025855 0.2272319     1     1
34 170.21519 3.634377 3.165426 4.103328 0.2283065     1     1
35 173.79747 3.712729 3.242093 4.183364 0.2291263     1     1
36 177.37975 3.813399 3.347232 4.279565 0.2269509     1     1
37 180.96203 3.910849 3.447572 4.374127 0.2255441     1     1
38 184.54430 3.977051 3.517784 4.436318 0.2235917     1     1
39 188.12658 4.037302 3.583959 4.490645 0.2207076     1     1
40 191.70886 4.091635 3.645111 4.538160 0.2173882     1     1
41 195.29114 4.140082 3.700184 4.579981 0.2141624     1     1
42 198.87342 4.182676 3.748159 4.617192 0.2115424     1     1
43 202.45570 4.219447 3.788162 4.650732 0.2099688     1     1
44 206.03797 4.250429 3.819579 4.681280 0.2097573     1     1
45 209.62025 4.275654 3.842137 4.709171 0.2110556     1     1
46 213.20253 4.295154 3.855951 4.734357 0.2138238     1     1
47 216.78481 4.308961 3.861497 4.756425 0.2178456     1     1
48 220.36709 4.317108 3.859541 4.774675 0.2227644     1     1
49 223.94937 4.319626 3.851025 4.788227 0.2281358     1     1
50 227.53165 4.316548 3.836964 4.796132 0.2334829     1     1
51 231.11392 4.308435 3.818728 4.798143 0.2384117     1     1
52 234.69620 4.302276 3.802201 4.802351 0.2434590     1     1
53 238.27848 4.297902 3.787395 4.808409 0.2485379     1     1
54 241.86076 4.292303 3.772103 4.812503 0.2532567     1     1
55 245.44304 4.282505 3.754087 4.810923 0.2572576     1     1
56 249.02532 4.269040 3.733184 4.804896 0.2608786     1     1
57 252.60759 4.253361 3.710042 4.796680 0.2645121     1     1
58 256.18987 4.235474 3.684476 4.786473 0.2682509     1     1
59 259.77215 4.215385 3.656265 4.774504 0.2722044     1     1
60 263.35443 4.193098 3.625161 4.761036 0.2764974     1     1
61 266.93671 4.168621 3.590884 4.746357 0.2812681     1     1
62 270.51899 4.141957 3.553134 4.730781 0.2866658     1     1
63 274.10127 4.113114 3.511593 4.714635 0.2928472     1     1
64 277.68354 4.082096 3.465939 4.698253 0.2999729     1     1
65 281.26582 4.048910 3.415849 4.681971 0.3082025     1     1
66 284.84810 4.013560 3.361010 4.666109 0.3176905     1     1
67 288.43038 3.976052 3.301132 4.650972 0.3285813     1     1
68 292.01266 3.936392 3.235952 4.636833 0.3410058     1     1
69 295.59494 3.894586 3.165240 4.623932 0.3550782     1     1
70 299.17722 3.850639 3.088806 4.612473 0.3708948     1     1
71 302.75949 3.804557 3.006494 4.602619 0.3885326     1     1
72 306.34177 3.756345 2.918191 4.594499 0.4080510     1     1
73 309.92405 3.706009 2.823813 4.588205 0.4294926     1     1
74 313.50633 3.653554 2.723308 4.583801 0.4528856     1     1
75 317.08861 3.598987 2.616650 4.581325 0.4782460     1     1
76 320.67089 3.542313 2.503829 4.580796 0.5055805     1     1
77 324.25316 3.483536 2.384853 4.582220 0.5348886     1     1
78 327.83544 3.422664 2.259739 4.585589 0.5661643     1     1
79 331.41772 3.359701 2.128512 4.590891 0.5993985     1     1
80 335.00000 3.294654 1.991200 4.598107 0.6345798     1     1

先验知道您想要的那个在列表中的位置并不容易，但如果没有别的，您可以查看列名称。

不过，最好在 ggplot 调用之外进行平滑处理。

编辑:

事实证明，复制 ggplot2 所做的事情来制作 loess 并不像我想象的那么简单，但这会奏效。我从ggplot2的一些内部函数中复制出来的。

model <- loess(wt ~ hp, data=mtcars)
xrange <- range(mtcars$hp)
xseq <- seq(from=xrange[1], to=xrange[2], length=80)
pred <- predict(model, newdata = data.frame(hp = xseq), se=TRUE)
y = pred$fit
ci <- pred$se.fit * qt(0.95 / 2 + .5, pred$df)
ymin = y - ci
ymax = y + ci
loess.DF <- data.frame(x = xseq, y, ymin, ymax, se = pred$se.fit)

ggplot(mtcars, aes(x=hp, y=wt)) +
  geom_point() +
  geom_smooth(aes_auto(loess.DF), data=loess.DF, stat="identity")

这给出了一个看起来相同的情节

ggplot(mtcars, aes(x=hp, y=wt)) +
  geom_point() +
  geom_smooth()

(这是原始 p 的扩展形式)。