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)
$ c
$ 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")
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