Method to extract stat_smooth line fit
A more general approach could be to simply use the predict() function to predict any range of values that are interesting.
# define the model
model <- loess(wt ~ hp, data = mtcars)
# predict fitted values for each observation in the original dataset
modelFit <- data.frame(predict(model, se = TRUE))
# define data frame for ggplot
df <- data.frame(cbind(hp = mtcars$hp
, wt = mtcars$wt
, fit = modelFit$fit
, upperBound = modelFit$fit + 2 * modelFit$se.fit
, lowerBound = modelFit$fit - 2 * modelFit$se.fit
))
# build the plot using the fitted values from the predict() function
# geom_linerange() and the second geom_point() in the code are built using the values from the predict() function
# for comparison ggplot's geom_smooth() is also shown
g <- ggplot(df, aes(hp, wt))
g <- g + geom_point()
g <- g + geom_linerange(aes(ymin = lowerBound, ymax = upperBound))
g <- g + geom_point(aes(hp, fit, size = 1))
g <- g + geom_smooth(method = "loess")
g
# Predict any range of values and include the standard error in the output
predict(model, newdata = 100:300, se = TRUE)
stat_smooth does produce output that you can use elsewhere, and with a slightly hacky way, you can put it into a variable in the global environment.
You enclose the output variable in .. on either side to use it. So if you add an aes in the stat_smooth call and use the global assign, <<-, to assign the output to a varible in the global environment you can get the the fitted values, or others - see below.
qplot(hp,wt,data=mtcars) + stat_smooth(aes(outfit=fit<<-..y..))
fit
[1] 1.993594 2.039986 2.087067 2.134889 2.183533 2.232867 2.282897 2.333626
[9] 2.385059 2.437200 2.490053 2.543622 2.597911 2.652852 2.708104 2.764156
[17] 2.821771 2.888224 2.968745 3.049545 3.115893 3.156368 3.175495 3.181411
[25] 3.182252 3.186155 3.201258 3.235698 3.291766 3.353259 3.418409 3.487074
[33] 3.559111 3.634377 3.712729 3.813399 3.910849 3.977051 4.037302 4.091635
[41] 4.140082 4.182676 4.219447 4.250429 4.275654 4.295154 4.308961 4.317108
[49] 4.319626 4.316548 4.308435 4.302276 4.297902 4.292303 4.282505 4.269040
[57] 4.253361 4.235474 4.215385 4.193098 4.168621 4.141957 4.113114 4.082096
[65] 4.048910 4.013560 3.976052 3.936392 3.894586 3.850639 3.804557 3.756345
[73] 3.706009 3.653554 3.598987 3.542313 3.483536 3.422664 3.359701 3.294654
The outputs you can obtain are:
y, predicted valueymin, lower pointwise confidence interval around the meanymax, upper pointwise confidence interval around the meanse, standard error
Note that by default it predicts on 80 data points, which may not be aligned with your original data.
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).