blocks|key|3871463|text|想一想，通过这样做，我可以与证明它所做的一切的结果相匹配：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3871464|library(plyr)
+iqr+<-+function(x,+...)+{
+++++qs+<-+quantile(as.numeric(x),+probs+=+c(0.25,+0.75),+na.rm+=+TRUE)
+++++names(qs)+<-+c("ymin","ymax")
+++++qs
+}
+ddply(DF,+.(cnd),+summarise,+new+=+iqr(bias))|code-block|syntax|javascript|3871465|但是这个例子突出了impot，conf.int参数是至关重要的。|offset|length|style|CODE|3871466|entityMap^0|0|0|F|8|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Q|8|@$I|R|J|S|K|L]]|9|@]|A|$]]|$1|M|3|-4|5|6|7|T|8|@]|9|@]|A|$]]]|N|$]]

Think by doing this I can match the results proving its doing what we think:

<pre><code>library(plyr)
 iqr &lt;- function(x, ...) {
 qs &lt;- quantile(as.numeric(x), probs = c(0.25, 0.75), na.rm = TRUE)
 names(qs) &lt;- c("ymin","ymax")
 qs
 }
 ddply(DF, .(cnd), summarise, new = iqr(bias))
</code></pre>

but this example highlights the impot the <code>conf.int</code> argument is crucial

blocks|key|1541302|text|median_hilow只是smedian_hilow的包装器，它来自于Hmisc包。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1541303|从来自smean+/+smedian的Hmisc函数组的文档。|1541304|如下面@BondedDust的评论所示，您需要在前面安装包Hmisc。|1541305|(输入?smedian_hilow和?median_hilow)：|1541306|提供了一些统计汇总函数，以便与summary.formula和汇总一起使用(以及tapply和它们自己)。smean.cl.normal计算了3个汇总变量:样本均值和基于t分布的上、下高斯置信限。smean.sd计算平均值和标准差。smean.sdl计算平均值加减一个常量乘以标准差。smean.cl.boot是一种快速实现的基本非参数自举，用于在不设正态的情况下获得种群均值的置信限。这些函数都会自动删除NAs。smedian.hilow计算样本中值和具有相等尾部区域的选定的一对外部分位数。
|blockquote|1541307|1541308|smedian.hilow根据置信区间计算中值、下四分位数和上四分位数。例如：|1541309|x+<-+rnorm(100)

>+smedian.hilow(x,+conf.int=.5)++#+25th+and+75th+percentiles
+++++Median+++++++Lower+++++++Upper+
+0.02036472+-0.76198947++0.71190404+|code-block|syntax|javascript|1541310|您可以查看@BondedDust关于如何使用ggplot2函数实现这个问题的答案。|1541311|entityMap^0|0|C|E|D|10|5|0|3|F|J|5|0|T|5|0|3|E|I|D|0|0|0|0|D|0|0|M|7|0^^$0|@$1|2|3|4|5|6|7|10|8|@$9|11|A|12|B|C]|$9|13|A|14|B|C]|$9|15|A|16|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|17|8|@$9|18|A|19|B|C]|$9|1A|A|1B|B|C]]|D|@]|E|$]]|$1|H|3|I|5|6|7|1C|8|@$9|1D|A|1E|B|C]]|D|@]|E|$]]|$1|J|3|K|5|6|7|1F|8|@$9|1G|A|1H|B|C]|$9|1I|A|1J|B|C]]|D|@]|E|$]]|$1|L|3|M|5|N|7|1K|8|@]|D|@]|E|$]]|$1|O|3|-4|5|6|7|1L|8|@]|D|@]|E|$]]|$1|P|3|Q|5|6|7|1M|8|@$9|1N|A|1O|B|C]]|D|@]|E|$]]|$1|R|3|S|5|T|7|1P|8|@]|D|@]|E|$U|V]]|$1|W|3|X|5|6|7|1Q|8|@$9|1R|A|1S|B|C]]|D|@]|E|$]]|$1|Y|3|-4|5|6|7|1T|8|@]|D|@]|E|$]]]|Z|$]]

<code>median_hilow</code> is just a wrapper around <code>smedian_hilow</code> which comes from the <code>Hmisc</code> package.

From the documentation of <code>smean / smedian</code> group of functions from <code>Hmisc</code>. 

As per @BondedDust 's comment below you need to have the package <code>Hmisc</code> installed previously.

(type <code>?smedian_hilow</code> and <code>?median_hilow</code>):

<blockquote>
 A number of statistical summary functions is provided for use with summary.formula and summarize (as well as tapply and by themselves). smean.cl.normal computes 3 summary variables: the sample mean and lower and upper Gaussian confidence limits based on the t-distribution. smean.sd computes the mean and standard deviation. smean.sdl computes the mean plus or minus a constant times the standard deviation. smean.cl.boot is a very fast implementation of the basic nonparametric bootstrap for obtaining confidence limits for the population mean without assuming normality. These functions all delete NAs automatically. smedian.hilow computes the sample median and a selected pair of outer quantiles having equal tail areas.
</blockquote>

The <code>smedian.hilow</code> calculates the median and lower and upper quartiles according to a confidence interval. As an example:

<pre><code>x &lt;- rnorm(100)

&gt; smedian.hilow(x, conf.int=.5) # 25th and 75th percentiles
 Median Lower Upper 
 0.02036472 -0.76198947 0.71190404 
</code></pre>

And you can have a look at @BondedDust's answer on exactly how this should be implemented with the <code>ggplot2</code> function.

blocks|key|1541361|text|如果您想要IQR，那么您就不需要median_hilow，至少它的默认值是这样的，因为它将低值作为2.5个百分位数，而高值作为97.5个百分位数。(伊拉克数量将分别为25和75)|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1541362|>+smedian.hilow(1:100)
Median++Lower++Upper+
50.500++3.475+97.525+|code-block|syntax|javascript|1541363|您可以使用0.5的conf.int-parameter以这种方式将-parameter传递给Hmisc::smedian.hilow-function，这将为您提供四分位数范围，因为(如Hmisc帮助页面所述)："+smedian.hilow+computes+the+sample+median+and+a+selected+pair+of+outer+quantiles+having+equal+tail+areas."：|1541364|ggplot(DF,aes(x=cnd,y=bias,colour=cnd))+%2B+
+++stat_summary(fun.data=median_hilow,+conf.int=.5)|1541365|entityMap^0|G|C|0|0|9|8|1A|K|2Y|2Z|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@$9|R|A|S|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|T|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|U|8|@$9|V|A|W|B|C]|$9|X|A|Y|B|C]|$9|Z|A|10|B|C]]|D|@]|E|$]]|$1|M|3|N|5|H|7|11|8|@]|D|@]|E|$I|J]]|$1|O|3|-4|5|6|7|12|8|@]|D|@]|E|$]]]|P|$]]

If you want the IQR then you do not want <code>median_hilow</code>, at least with its defaults, because it delivers the low value as the 2.5th percentile and the high value as the 97.5th percentile. (IQR would be 25th and 75th.)

<pre><code>&gt; smedian.hilow(1:100)
Median Lower Upper 
50.500 3.475 97.525 
</code></pre>

You can pass the <code>conf.int</code>-parameter to the <code>Hmisc::smedian.hilow</code>-function in this manner using conf.int of 0.5 which will give you the interquartile ranges because (as the Hmisc help page says): <code>" smedian.hilow computes the sample median and a selected pair of outer quantiles having equal tail areas."</code> :

<pre><code>ggplot(DF,aes(x=cnd,y=bias,colour=cnd)) + 
 stat_summary(fun.data=median_hilow, conf.int=.5)
</code></pre>

<pre><code>set.seed(42)
DF &lt;- data.frame(bias=rnorm(2700),cnd=1:27)
DF$cnd &lt;- factor(DF$cnd)
</code></pre>

Trying to understand the use of median_hilow in ggplot. I was hoping to find a way to plot upper and lower interquartile ranges. But I can't find the a full explanation for 'fun.data=median_hilow' anywhere. Even though I assume it is doing the correct thing. Is there any full documentation for this function to check how it is plotting IQRs?

<pre><code>library(ggplot2)
ggplot(DF,aes(x=cnd,y=bias,colour=cnd)) + 
 stat_summary(fun.data=median_hilow)
</code></pre>

interquartile ranges in ggplot2

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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set.seed(42)DF <- data.frame(bias=rnorm(2700),cnd=1:27)DF$cnd <- factor(DF$cnd)试图理解median_hilow在ggplot中的使用。我希望找到一种方法来绘制上下四分位数的范围。但我在任何地方都找不到“fun.data=median_hil...

问ggplot2中的四分位数范围
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