基于r中的图检索x和y值 [英] retrieve x and y value based on graph in r

问题描述

我是r的新手，我想请大家帮忙.我有x(值)和prob(概率)，如下所示:

I'm new in r and I would ask you all some help. I have x (value) and prob (it's probability) as follow:

• x<-c(0.00，1.08，2.08，3.08，4.08，4.64，4.68)
• 概率<-c(0.000，0.600，0.370，0.010，0.006，0.006，0.006)

我的目标是根据这些值构造一个估计分布图.到目前为止，我使用 qplot(x，prob，geom = c("point"，"smooth")，span = 0.55)进行制作，如下所示 https://i.stack.imgur.com/aVgNk.png

My aim is to contruct an estimate distribution graph based on those values. So far, I use qplot(x,prob,geom=c("point", "smooth"),span=0.55) to make it and it's shown here https://i.stack.imgur.com/aVgNk.png

我的问题是:

1. 是否有其他方法可以构建像这样的不错的发行版不使用qplot?
2. 我需要检索所有x值(即0.5、1、1.2等)及其对应的概率值.我可以那样做吗?

我已经搜索了一段时间，但是没有运气.

I've been searching for a while, but with no luck.

谢谢大家

推荐答案

如果要为给定的 x 值预测 prob 的值，则为一种方法.请注意，我在这里使用的是黄土预测函数(因为我相信这是 ggplot 的 smooth geom的默认设置)，可能不适合您.

If you're looking to predict the values of prob for given values of x, this is one way to do it. Note I'm using a loess prediction function here (because I believe it's the default for ggplot's smooth geom, which you've used), which may or may not be appropriate for you.

x <- c(0.00, 1.08, 2.08, 3.08, 4.08, 4.64, 4.68)
prob <- c(0.000, 0.600, 0.370, 0.010, 0.006, 0.006, 0.006)

首先创建一个只有一列的数据框，然后将大量数据点放到该列中，只是作一堆预测.

First make a data frame with one column, I'll put a whole lot of data points into that column, just to make a bunch of predictions.

df <- data.frame( datapoints = seq.int( 0, max(x), 0.1 ) )

然后创建一个预测列.我正在使用 predict 函数，将 loess 平滑函数传递给它.将为 loess 函数提供输入数据，并要求 predict 使用 loess 中的函数来预测 df的值$ datapoints

Then create a prediction column. I'm using the predict function, passing a loess smoothed function to it. The loess function is given your input data, and predict is asked to use the function from loess to predict for the values of df$datapoints

df$predicted <- predict( loess( prob ~ x, span = 0.55 ), df$datapoints )

这是输出的样子.

> head( df )
  datapoints  predicted
1        0.0 0.01971800
2        0.1 0.09229939
3        0.2 0.15914675
4        0.3 0.22037484
5        0.4 0.27609841
6        0.5 0.32643223

在绘图方面， ggplot2 是一个不错的选择，因此在这里我不认为有理由回避 qplot .如果希望从 ggplot2 中获得更多的灵活性，则可以更明确地编写功能(如@Jan Sila在另一个答案中提到的那样).这是 ggplot2 的更常见(更灵活)语法的一种方式:

On the plotting side of things, ggplot2 is a good way to go, so I don't see a reason to shy away from qplot here. If you want more flexibility in what you get from ggplot2, you can code the functions more explicitly (as @Jan Sila has mentioned in another answer). Here's a way with ggplot2's more common (and more flexible) syntax:

plot <- ggplot( data = df, 
                mapping = aes( x = datapoints, 
                               y = predicted ) ) +
    geom_point() +
    geom_smooth( span = 0.55 )
plot

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