问题描述
我有一个实验设计.我想计算其D效率.我认为R包AlgDesign可以提供帮助.我发现函数optFederov可以生成设计，并且-如果用户需要-可以返回其D效率.但是，我不想使用optFederov生成设计-我已经有了我的设计！我尝试了eval.design(〜.，mydesign).但是它给我的唯一指标是:行列式，A，对角线和gmean.variances.也许有一种方法可以使行列式或A转换为D效率(我不是数学家，所以不确定).或者说可以使用其他方式手动"计算D效率?
I have an experimental design. I want to calculate its D-efficiency.I thought R package AlgDesign could help. I found function optFederov which generates the design and - if the user wants - returns its D efficiency. However, I don't want to use optFederov to generate the design - I already have my design!I tried eval.design(~.,mydesign). But the only metrics it gives me are: determinant, A, diagonality, and gmean.variances. Maybe there is a way to get from determinant or A to D-efficiency (I am not a mathematician, so I am not sure). Or maybe some other way to calculate D-efficiency "manually" so to say?
非常感谢任何提示！
推荐答案
我正在从事类似的项目.我在此链接.其中X是模型矩阵，p是线性模型中的beta数，ND是实验的运行次数.您可以编写这样的代码，它就能解决问题.
I was working on a similar project. I found out this formula Deff = (|X'X|^(1/p))/ND in this link. Where X is the model matrix, p is the number of betas in you linear model and ND the number of runs your experiment has. You could just make a code like this and it will do the trick.
det(t(X)%*%X)^(1/beta)/(numRuns)
我在项目中使用JMP测试了结果，因此我相信这是正确的公式
I tested the results using JMP for my project so I believe this is the correct formula
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