在R的点阵封装中微调点图 [英] Fine tuning a dotplot in R's lattice package

问题描述

我正在尝试为不同的数据集和不同的算法绘制一堆ROC区域. 我有三个变量:"Scheme"，它指定使用的算法；"Dataset"，是要在其上测试算法的数据；以及"Area_under_ROC".

I am trying to plot a bunch of ROC areas for different datasets and different algorithms. I have three variables: "Scheme" which specifies the algorithm used, "Dataset" is the dataseet that the algorithm is being tested on, and "Area_under_ROC".

我正在R中使用晶格库，并使用以下命令:

I am using the lattice library in R, with the following command:

dotplot(方案〜Area_under_ROC |数据集，数据=模拟摘要，布局= c(4,6))

dotplot(Scheme ~ Area_under_ROC | Dataset, data = simulationSummary, layout = c(4,6))

这就是我得到的:

基于数据集的方案与Area_under_ROC的点图

我想知道的是

• 如何使y轴上的标签可读?现在，他们都被挤在一起了.
• 如何重新排列面板，使标记为"100"的数据集形成最后一列，而其他列保持不变?

非常感谢您提出任何意见或建议. 非常感谢！

I'd very much appreciate any comments or pointers. Many Thanks!

推荐答案

一些想法:

1. 为y轴标签使用较小的字体，例如scale=list(y=list(cex=.6)).一种替代方法是保留统一的字体大小，但将输出分成几页(可以用layout=进行控制)，或者更好的方法是显示同一数据集中的所有数据(从A到F，因此4点表示).每种算法)或样本大小(10到100，因此每个算法6点)，并带有group=选项.为此，我将亲自创建两个因素，分别为sample.size和dataset.type.

2. 重新设置因子Dataset，以便您感兴趣的数据集出现在layout放置它们的位置，或者(更好！)使用index.cond为24个面板指定特定排列.例如

1. Use a smaller font size for y-axis labels, e.g. scale=list(y=list(cex=.6)). An alternative would be to preserve uniform font size, but separate your output on several pages (this can be controlled with layout=), or, probably better, to show all data from the same dataset (A to F, hence 4 points for each algorithm) or by sample size (10 to 100, hence 6 points for each algorithm) with a group= option. I would personally create two factors, sample.size and dataset.type for that.

2. Relevel your factor Dataset, so that the dataset you are interested appear where layout will put them, or (better!) use index.cond to specify a particular arrangement for your 24 panels. E.g.,

dfrm <- data.frame(algo=gl(11, 1, 11*24, labels=paste("algo", 1:11, sep="")), 
                   type=gl(24, 11, 11*24, labels=paste("type", 1:24, sep="")),
                   roc=runif(11*24))
p <- dotplot(algo ~ roc | type, dfrm, layout=c(4,6), scale=list(y=list(cex=.4)))

将按从左下到右上的顺序排列面板(左下面板中的type1，右上面板中的type24)，而

will arrange panels in sequential order, from bottom left to top right (type1 in bottom left panel, type24 in top right panel), while

update(p, index.cond=list(24:1))

将以相反的顺序排列面板.只需指定具有预期面板位置的list.

will arrange panels in the reverse order. Just specify a list with expected panel locations.

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这里是一个例子，说明了我对要点1的想法，并使用了两个因素而不是一个.让我们生成另一个人工数据集:

---

Here is an example of what I have in mind with Point 1 and the use of two factors instead of one. Let us generate another artificial dataset:

dfrm <- data.frame(algo=gl(11, 1, 11*24, labels=paste("algo", 1:11, sep="")),
                   dataset=gl(6, 11, 11*24, labels=LETTERS[1:6]),
                   ssize=gl(4, 11*6, 11*24, labels=c(10,25,50,100)), 
                   roc=runif(11*24))
xtabs(~ dataset + ssize, dfrm)  # to check allocation of factor levels 
dotplot(algo ~ roc | dataset, data=dfrm, group=ssize, type="l", 
        auto.key=list(space="top", column=4, cex=.8, title="Sample size", 
                      cex.title=1, lines=TRUE, points=FALSE))

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