### 统计代写|R语言代写R language代考|NTRES6100

R是一种用于统计计算和图形的编程语言，由R核心团队和R统计计算基金会支持。R由统计学家Ross Ihaka和Robert Gentleman创建，在数据挖掘者和统计学家中被用于数据分析和开发统计软件。用户已经创建了软件包来增强R语言的功能。

statistics-lab™ 为您的留学生涯保驾护航 在代写R语言方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写R语言代写方面经验极为丰富，各种代写R语言相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 统计代写|R语言代写R language代考|Using R interactively

A physical terminal (keyboard plus text-only screen) decades ago was how users communicated with computers, and was frequently called a console. Nowadays, a text-only interface to a computer, in most cases a window or a pane within a graphical user interface, is still called a console. In our case, the R console (Figure 1.1). This is the native user interface of $R$.

Typing commands at the $\mathrm{R}$ console is useful when one is playing around, rather aimlessly exploring things, or trying to understand how an R function or operator we are not familiar with works. Once we want to keep track of what we are doing, there are better ways of using $\mathrm{R}$, which allow us to keep a record of how an analysis has been carried out. The different ways of using R are not exclusive of each other, so most users will use the $\mathrm{R}$ console to test individual commands and plot data during the first stages of exploration. As soon as we decide how we want to plot or analyze the data, it is best to start using scripts. This is not enforced in any way by $\mathrm{R}$, but scripts are what really brings to light the most important advantages of using a programming language for data analysis. In Figure $1.1$ we can see how the $\mathrm{R}$ console looks. The text in red has been typed in by the user, except for the prompt $>$, and the text in blue is what $\mathrm{R}$ has displayed in response. It is essentially a dialogue between user and $\mathrm{R}$. The console can look different when displayed within an IDE like RStudio, but the only difference is in the appearance of the text rather than in the text itself (cf. Figures $1.1$ and 1.2).

The two previous figures showed the result of entering a single command. Figure $1.3$ shows how the console looks after the user has entered several commands, each as a separate line of text.

The examples in this book require only the console window for user input. Menu-driven programs are not necessarily bad, they are just unsuitable when there is a need to set very many options and choose from many different actions. They are also difficult to maintain when extensibility is desired, and when independently developed modules of very different characteristics need to be integrated. Textual languages also have the advantage, to be addressed in later chapters, that command sequences can be stored in human- and computer-readable text files. Such files constitute a record of all the steps used, and in most cases, makes it trivial to reproduce the same steps at a later time. Scripts are a very simple and handy way of communicating to other users how to do a given data analysis.

## 统计代写|R语言代写R language代考|Using R in a “batch job”

To run a script we need first to prepare a script in a text editor. Figure $1.4$ shows the console immediately after running the script file shown in the text editor. As before, red text, the command source(“my-script.R”), was typed by the user, and the blue text in the console is what was displayed by $\mathrm{R}$ as a result of this action. The title bar of the console, shows “R-console,” while the title bar of the editor shows the path to the script file that is open and ready to be edited followed by “R-editor.”

A true “batch job” is not run at the R console but at the operating system command prompt, or shell. The shell is the console of the operating system-Linux, Unix, OS X, or MS-Windows. Figure $1.5$ shows how running a script at the Windows command prompt looks. A script can be run at the operating system prompt to do time-consuming calculations with the output saved to a file. One may use this approach on a server, say, to leave a large data analysis job running overnight or even for several days.

Integrated Development Environments (IDEs) are used when developing computer programs. IDEs provide a centralized user interface from within which the different tools used to create and test a computer program can be accessed and used in coordination. Most IDEs include a dedicated editor capable of syntax highlighting, and even report some mistakes, related to the programming language in use. One could describe such an editor as the equivalent of a word processor with spelling and grammar checking, that can alert about spelling and syntax errors for a computer language like $R$ instead of for a natural language like English. In the case of RStudio, the main, but not only language supported is $\mathrm{R}$. The main window of IDEs usually displays more than one pane simultaneously. From within the RStudio IDF, one has access to the R console, a text editor, a file-system hrowser, a pane for graphical output, and access to several additional tools such as for installing and updating extension packages. Although RStudio supports very well the development of large scripts and packages, it is currently, in my opinion, also the hest possihle way of using $\mathrm{R}$ at the console as it has the $\mathrm{R}$ help system very well integrated both in the editor and $\mathrm{R}$ console. Figure $1.6$ shows the main window displayed by RStudio after running the same script as shown above at the $\mathrm{R}$ console (Figure 1.4) and at the operating system command prompt (Figure 1.5). We can see by comparing these three figures how RStudio is really a layer between the user and an unmodified R executable. The script was sourced by pressing the “Source” button at the top of the editor pane. RStudio, in response to this, generated the code needed to source the file and “entered” it at the console, the same console, where we would type any $\mathrm{R}$ commands.

## 广义线性模型代考

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## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。