[英]Find points over and under the confidence interval when using geom_stat / geom_smooth in ggplot2
I have a scatter plot,I want to know how can I find the genes above and below the confidence interval lines? 我有一个散点图,我想知道如何在置信区间线上方和下方找到基因?
EDIT: Reproducible example: 编辑:可重复的例子:
library(ggplot2)
#dummy data
df <- mtcars[,c("mpg","cyl")]
#plot
ggplot(df,aes(mpg,cyl)) +
geom_point() +
geom_smooth()
This solution takes advantage of the hard work ggplot2 does for you: 这个解决方案利用了ggplot2为您做的辛勤工作:
library(sp)
# we have to build the plot first so ggplot can do the calculations
ggplot(df,aes(mpg,cyl)) +
geom_point() +
geom_smooth() -> gg
# do the calculations
gb <- ggplot_build(gg)
# get the CI data
p <- gb$data[[2]]
# make a polygon out of it
poly <- data.frame(
x=c(p$x[1], p$x, p$x[length(p$x)], rev(p$x)),
y=c(p$ymax[1], p$ymin, p$ymax[length(p$x)], rev(p$ymax))
)
# test for original values in said polygon and add that to orig data
# so we can color by it
df$in_ci <- point.in.polygon(df$mpg, df$cyl, poly$x, poly$y)
# re-do the plot with the new data
ggplot(df,aes(mpg,cyl)) +
geom_point(aes(color=factor(in_ci))) +
geom_smooth()
It needs a bit of tweaking (ie that last point getting a 2
value) but I'm limited on time. 它需要一些调整(即最后一点获得2
值),但我的时间有限。 NOTE that the point.in.polygon
return values are: 请注意, point.in.polygon
返回值为:
0
: point is strictly exterior to pol 0
:点是pol的外部 1
: point is strictly interior to pol 1
:点是pol的内部 2
: point lies on the relative interior of an edge of pol 2
:点位于pol边缘的相对内部 3
: point is a vertex of pol 3
:点是pol的顶点 so it should be easy to just change the code to TRUE
/ FALSE
whether value is 0
or not. 所以将代码更改为TRUE
/ FALSE
应该很容易,无论值是否为0
。
I had to take a deep dive into the github
repo but I finally got it. 我不得不深入了解github
回购,但我终于得到了它。 In order to do this you need to know how stat_smooth
works. 为此,您需要了解stat_smooth
工作原理。 In this specific case the loess
function is called to do the smoothing (the different smoothing functions can be constructed using the same process as below): 在这种特定情况下,调用loess
函数进行平滑(可以使用与下面相同的过程构造不同的平滑函数):
So, using loess
on this occasion we would do: 所以,在这个场合使用loess
,我们会这样做:
#data
df <- mtcars[,c("mpg","cyl"), with=FALSE]
#run loess model
cars.lo <- loess(cyl ~ mpg, df)
Then I had to read this in order to see how the predictions are made internally in stat_smooth
. 然后我必须阅读这个 ,以便了解如何在stat_smooth
内部进行stat_smooth
。 Apparently hadley uses the predictdf
function (which is not exported to the namespace) as follows for our case: 显然,hadley使用predictdf
函数(未导出到命名空间),如下所示:
predictdf.loess <- function(model, xseq, se, level) {
pred <- stats::predict(model, newdata = data.frame(x = xseq), se = se)
if (se) {
y = pred$fit
ci <- pred$se.fit * stats::qt(level / 2 + .5, pred$df)
ymin = y - ci
ymax = y + ci
data.frame(x = xseq, y, ymin, ymax, se = pred$se.fit)
} else {
data.frame(x = xseq, y = as.vector(pred))
}
}
After reading the above I was able to create my own data.frame of the predictions using: 阅读完上述内容后,我可以使用以下方法创建自己的数据预测框架:
#get the predictions i.e. the fit and se.fit vectors
pred <- predict(cars.lo, se=TRUE)
#create a data.frame from those
df2 <- data.frame(mpg=df$mpg, fit=pred$fit, se.fit=pred$se.fit * qt(0.95 / 2 + .5, pred$df))
Looking at predictdf.loess
we can see that the upper boundary of the confidence interval is created as pred$fit + pred$se.fit * qt(0.95 / 2 + .5, pred$df)
and the lower boundary as pred$fit - pred$se.fit * qt(0.95 / 2 + .5, pred$df)
. 看看predictdf.loess
我们可以看到置信区间的上边界被创建为pred$fit + pred$se.fit * qt(0.95 / 2 + .5, pred$df)
,下边界为pred$fit - pred$se.fit * qt(0.95 / 2 + .5, pred$df)
。
Using those we can create a flag for the points over or below those boundaries: 使用那些我们可以为这些边界之上或之下的点创建一个标志:
#make the flag
outerpoints <- +(df$cyl > df2$fit + df2$se.fit | df$cyl < df2$fit - df2$se.fit)
#add flag to original data frame
df$outer <- outerpoints
The df$outer
column is probably what the OP is looking for (it takes the value of 1 if it is outside the boundaries or 0 otherwise) but just for the sake of it I am plotting it below. df$outer
列可能是OP正在查找的内容(如果它在边界之外则取值1,否则为0)但仅仅是为了它我正在下面绘制它。
Notice the +
function above is only used here to convert the logical flag into a numeric. 请注意,上面的+
函数仅用于将逻辑标志转换为数字。
Now if we plot as this: 现在,如果我们绘制如下:
ggplot(df,aes(mpg,cyl)) +
geom_point(aes(colour=factor(outer))) +
geom_smooth()
We can actually see the points inside and outside the confidence interval. 我们实际上可以看到置信区间内外的点。
Output: 输出:
PS For anyone who is interested in the upper and lower boundaries, they are created like this (speculation: although the shaded areas are probably created with geom_ribbon - or something similar - which makes them more round and pretty): PS对于那些对上下边界感兴趣的人,他们是这样创造的(推测:虽然阴影区域可能是用geom_ribbon创建的 - 或类似的东西 - 这使得它们更圆而且漂亮):
#upper boundary
ggplot(df,aes(mpg,cyl)) +
geom_point(aes(colour=factor(outer))) +
geom_smooth() +
geom_line(data=df2, aes(mpg , fit + se.fit , group=1), colour='red')
#lower boundary
ggplot(df,aes(mpg,cyl)) +
geom_point(aes(colour=factor(outer))) +
geom_smooth() +
geom_line(data=df2, aes(mpg , fit - se.fit , group=1), colour='red')
Using ggplot_build
like @hrbrmstr's nice solution, you can actually do this by simply passing a sequence of x values to geom_smooth
specifying where the errors bounds should be calculated, and make this equal to the x-values of your points. 使用ggplot_build
就像@ hrbrmstr这个不错的解决方案一样,你可以通过简单地将一系列x值传递给geom_smooth
指定应该计算错误界限的位置来实现这一点,并使其等于你的点的x值。 Then, you just see if the y-values are within the range. 然后,您只需查看y值是否在范围内。
library(ggplot2)
## dummy data
df <- mtcars[,c("mpg","cyl")]
ggplot(df, aes(mpg, cyl)) +
geom_smooth(params=list(xseq=df$mpg)) -> gg
## Find the points within bounds
bounds <- ggplot_build(gg)[[1]][[1]]
df$inside <- with(df, bounds$ymin < cyl & bounds$ymax > cyl)
## Add the points
gg + geom_point(data=df, aes(color=inside)) + theme_bw()
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