这种可定制的数据分析工具可生成 Bland-Altman 和相关散点图。 可以使用 2D 或 3D 矩阵表示法使用组的颜色和形状编码来显示数据。 数据点也可以使用序列编号显示，便于进一步询问异常值。 分析结果（例如相关系数、最佳拟合线、再现性系数等）可以显示在图形上。 统计结果作为结构化输出参数返回。 包括一个例子。

感谢作者潘文超博士 1.果蝇优化算法基本概念 2.果蝇优化算法解极大值和极小值 3.财务预警 Z-score 模型系数优化 4.广义/灰色神经网络优化 5.支援向量回归参数优化 6.果蝇最佳化算法的进阶微调

The MATLAB® programming environment is often perceived as a platform suitable for prototyping and modeling but not for actual real-life applications. One of the reasons that I constantly hear when consulting with clients is that “MATLAB is slow”. This book aims to help reduce this perception and shows that MATLAB programs can in fact be made to run extremely fast, in a wide variety of different ways. MathWorks, who develop MATLAB, invests a significant amount of R&D effort in constantly improving MATLAB’s performance and advocating best practices for improved performance.1 Postings for performance-related R&D jobs are periodically posted2 and the engine’s performance improves with almost every semi-annual MATLAB release. In fact, the same MATLAB programs that might have been slow 10 or more years ago may now be blazingly fast when run using the latest MATLAB release, on the very same platform. Using programming techniques presented in this book, MATLAB applications can be made even faster, fast enough for most uses. This enables significant reduction of the development time and cost, since we can use MATLAB from end to end, from prototyping to deployment, without having to maintain a mirror code–base using a different programming language and environment.