重新创建minitab正常概率图 [英] Recreate minitab normal probability plot

问题描述

.imgur.com / Ain27.jpgalt =alt text>

The probplot 可以让你获得大部分途径。不幸的是，我无法弄清楚如何在此图上添加置信区间带。

同样，ggplot的 stat_qq（）似乎呈现了一个变换后的x轴的类似信息。看来 geom_smooth（）可能会成为添加乐队的候选人，但我还没有弄清楚。

最后，获得遗传学完成家伙描述了类似的一些类似的东西。 htmlrel =nofollow noreferrer>>

示例数据重新创建上面的图表：

  x <-c（40.2,43.1,45.5,44.5,39.5,38.5,40.2,41.0,41.6,43.1,44.9,42.8）
  
 
 $ b 
如果任何人有基本图形或ggplot的解决方案，我会很感激它！
 
 
编辑 
 
 
 
在查看 probplot 的详细信息后，我已经确定这是它如何在图上生成拟合线： 
 
 
 > x1 < - 分位数（x，c（0.25,0.75））
> （c（0.25，0.75））
>斜率<  -  diff（yl）/ diff（xl）
> int<  -  yl [1]  - 斜率* x1 [1] 
>斜坡
 75％
 0.4151 
> int 
 75％
 -17.36 
  
的确，将这些结果与您出来probplot对象似乎比较好：
 >检查<  -  probplot（x）
> str（check）
 3 
 $的列表qdist：function（p）
 $ int：命名的数字-17.4 
 ..- attr（*，names）= chr 75％
 $ slope：命名数字0.415 
 ..- attr（*，names）= chr75％
  -  attr（*，class）= chr probplot
> 
  
但是，将这些信息合并到ggplot2或基本图形中并不会产生相同的结果。
  probplot（x）
  
  
 
 
 
对比： p> 
 
 
  ggplot（data = df，aes（x = x，y = y））+ geom_point（）+ geom_abline（intercept = int，斜坡=斜率）
  
  
 
 <$ p $ （$）
 $（$ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ p> 
 
 
最后，我了解到图例的最后两行是指Anderson-Darling测试的正常性，可以用 nortest 包。
 > ad.test（x）
 
 Anderson-Darling常态测试
 
数据：x 
 A = 0.2303，p值= 0.7502 
  
 
 
解决方案
也许这是您可以建立的基础。默认情况下，stat_smooth（）使用level = 0.95。
 
 
  df < -  data.frame（sort（x），ppoints（ x））
 colnames（df）< -c（x，y）
 
 ggplot（df，aes（x，y））+ 
 geom_point ）+ 
 stat_smooth（）+ 
 scale_y_continuous（limits = c（0,1），breaks = seq（from = 0.05，to = 1，by = 0.05），formatter =percent）
  
 
I am trying to recreate the following plot with R. Minitab describes this as a normal probability plot.



The probplot gets you most of the way there. Unfortunately, I cannot figure out how to add the confidence interval bands around this plot.

Similarly, ggplot's stat_qq() seems to present similar information with a transformed x axis. It seems that geom_smooth() would be the likely candidate to add the bands, but I haven't figure that out.

Finally, the Getting Genetics Done guys describe something similar here.

Sample data to recreate the plot above:
x <- c(40.2, 43.1, 45.5, 44.5, 39.5, 38.5, 40.2, 41.0, 41.6, 43.1, 44.9, 42.8)
If anyone has a solution with base graphics or ggplot, I'd appreciate it!

EDIT

After looking at the details of probplot, I've determined this is how it generates the fit line on the graph:
> xl <- quantile(x, c(0.25, 0.75))
> yl <- qnorm(c(0.25, 0.75))
> slope <- diff(yl)/diff(xl)
> int <- yl[1] - slope * xl[1]
> slope
   75% 
0.4151 
> int
   75% 
-17.36 
Indeed, comparing these results to what you get out of the probplot object seem to compare very well:
> check <- probplot(x)
> str(check)
List of 3
 $ qdist:function (p)  
 $ int  : Named num -17.4
  ..- attr(*, "names")= chr "75%"
 $ slope: Named num 0.415
  ..- attr(*, "names")= chr "75%"
 - attr(*, "class")= chr "probplot"
> 
However, incorporating this information into ggplot2 or base graphics does not yield the same results.
probplot(x)


Versus:
ggplot(data = df, aes(x = x, y = y)) + geom_point() + geom_abline(intercept = int, slope = slope)


I get similar results using R's base graphics
plot(df$x, df$y)
abline(int, slope, col = "red")
Lastly, I've learned that the last two rows of the legend refer to the Anderson-Darling test for normality and can be reproduced with the nortest package.
> ad.test(x)

    Anderson-Darling normality test

data:  x 
A = 0.2303, p-value = 0.7502

解决方案
Perhaps this will be something you can build on. By default, stat_smooth() uses level=0.95.
df <- data.frame(sort(x), ppoints(x))
colnames(df) <- c("x","y")

ggplot(df, aes(x,y)) + 
geom_point() + 
stat_smooth() + 
scale_y_continuous(limits=c(0,1),breaks=seq(from=0.05,to=1,by=0.05), formatter="percent")


                        
这篇关于重新创建minitab正常概率图的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！