R – Method to extract stat_smooth line fit

ggplot2rstat

Is there a way to extract the values of the fitted line returned from stat_smooth?

The code I am using looks like this:

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

This new r user would greatly appreciate any guidance.

Best Solution

Riffing off of @James example

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

You can use the intermediate stages of the ggplot building process to pull out the plotted data. The results of ggplot_build is a list, one component of which is data which is a list of dataframes which contain the computed values to be plotted. In this case, the list is two dataframes since the original qplot creates one for points and the stat_smooth creates a smoothed one.

> 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

Knowing a priori where the one you want is in the list isn't easy, but if nothing else you can look at the column names.

It is still better to do the smoothing outside the ggplot call, though.

EDIT:

It turns out replicating what ggplot2 does to make the loess is not as straightforward as I thought, but this will work. I copied it out of some internal functions in ggplot2.

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")

That gives a plot that looks identical to

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

(which is the expanded form of the original p).

Related Question