### 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|Subsetting

It is very common to want to extract one or more elements from a vector. For this, we use a technique called indexing or subsetting. After the vector, we put an integer in square brackets ( [] ) called the subscript operator. This instructs $\mathrm{R}$ to return the element at that index. The indices (plural for index, in case you were wondering!) for vectors in $\mathrm{R}$ start at 1 , and stop at the length of the vector.
$>$ our.vect[1] $\quad #$ to get the first value
[1] 8

$>$ # the function length() returns the length of a vector
$>$ length (our.vect)
[1] 7
$>$ our.vect [length (our.vect)] # get the last element of a vector
[1] 9
Note that in the preceding code, we used a function in the subscript operator. In cases like these, R evaluates the expression in the subscript operator, and uses the number it returns as the index to extract.

If we get greedy, and try to extract an element at an index that doesn’t exist, $\mathrm{R}$ will respond with NA, meaning, not available. We see this special value cropping up from time to time throughout this text.
$>$ our.vect [10]
[1] NA
One of the most powerful ideas in $\mathrm{R}$ is that you can use vectors to subset other vectors:
$>$ # extract the first, third, fifth, and
$>$ # seventh element from our vector
$>$ our.vect $[c(1,3,5,7)]$
The ability to use vectors to index other vectors may not seem like much now, but its usefulness will become clear soon.
Another way to create vectors is by using sequences.
Above, the $1: 10$ statement creates a vector from 1 to 10 . $10: 1$ would have created the same 10 element vector, but in reverse. The seq () function is more general in that it allows sequences to be made using steps (among many other things).

Did I mention that we can use vectors to subset other vectors? When we subset vectors using logical vectors of the same length, only the elements corresponding to the TRUE values are extracted. Hopefully, sparks are starting to go off in your head. If we wanted to extract only the legitimate non-NA digits from Jenny’s number, we can do it as follows:
$>$ messy.vector[1is.na (messy.vector)]
This is a very critical trait of $\mathrm{R}$, so let’s take our time understanding it; this idiom will come up again and again throughout this book.

The logical vector that yields TRUE when an NA value occurs in messy .vector (from is . na ()) is then negated (the whole thing) by the negation operator !. The resultant vector is TRUE whenever the corresponding value in messy. vector is not NA.
When this logical vector is used to subset the original messy vector, it only extracts the non-NA values from it.

Similarly, we can show all the digits in Jenny’s phone number that are greater than five as follows:
$>$ our.vect [our.vect $>$ 5]
Thus far, we’ve only been displaying elements that have been extracted from a vector. However, just as we’ve been assigning and re-assigning variables, we can assign values to various indices of a vector, and change the vector as a result. For example, if Jenny tells us that we have the first digit of her phone number wrong (it’s really 9), we can reassign just that element without modifying the others.
$>$ our.vect
[1] $8 \begin{array}{llllllll} & 6 & 7 & 5 & 3 & 0 & 9\end{array}$
$>$ our.vect [1] $<-9$ $>$ our.vect
Sometimes, it may be required to replace all the NA values in a vector with the value o. To do that with our messy vector, we can execute the following command:
$>$ messy.vector [is.na (messy.vector)] $<-0$

messy.vector
[1] $8 \begin{array}{llllllllll} & 8 & 0 & 7 & 5 & 0 & 3 & 0 & 9\end{array}$

## 统计代写|R语言代写R language代考|Subsetting

$>$ 我们的.vect[1] 四#得到第一个值
[1] 8
$>$ #函数 length() 返回向量的长度
$>$ 长度 (our.vect)
[1] 7
$>$ our.vect [length (our.vect)] # 获取向量的最后一个元素
[1] 9

$>$ our.vect [10]
[1] NA

$>$ #提取第一、第三、第五和
$>$ #向量中的第七个元素
$>$ 我们的.vect $[c(1,3,5,7)]$

$>$ messy.vector[1is.na (messy.vector)]

$>$ 我们的.vect [我们的.vect $>5$ ]

$>$ 我们的.vect
$>$ 我们的.vect $[1]<-9>$ our.vect

$>$ messy.vector $[$ is.na (messy.vector) $]<-0$

$\left[\begin{array}{lllllllll}{[1] 8} & 8 & 0 & 7 & 5 & 0 & 3 & 0 & 9\end{array}\right.$

## 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。