Matrices. Description: 'matrix' creates a matrix from the given set of values. Usage: matrix(data = NA, nrow = 1, ncol = 1, byrow = FALSE). 1.7 Printing and Saving Your Work. You can print directly from the R Console by selecting “Print…” in the File menu, but this will capture everything (including errors). Обыграть европейскую рулетку Программа Wheel Daemon Chains. Онлайн казино.Рулетка. 46 просмотров • год назад. ++NEW++ программа Wheel Daemon Chains. Игра в казино 888. +. R-Matrix VARIATOR Некоторое время назад, когда я официально презентовал дополнение к Wheel Daemon 4.0, которое получило название Chains, . Live roulette prediction. R - Matrix 1.7. mode 'R-smart1' Программы; Реклама · Монетизировать. Помощь; Семейный фильтр включен.

An Introduction to RAn Introduction to RTable of Contents. An Introduction to RThis is an introduction to R (“GNU S”), a language and environment for. R is similar to the. S. system, which was developed at Bell Laboratories by John Chambers et al. It provides a wide variety of statistical and graphical techniques. This manual provides information on data types, programming elements. This manual is for R, version 3.

Copyright © 1. 99. W. N. Venables. Copyright © 1. W. N. Venables & D. M. Smith. Copyright © 1. R. Gentleman & R. Ihaka. Copyright © 1.

M. Maechler. Copyright © 1. R Core Team. Permission is granted to make and distribute verbatim copies of this.

Permission is granted to copy and distribute modified versions of this. Permission is granted to copy and distribute translations of this manual. R Core Team. Preface. This introduction to R is derived from an original set of notes. S and S- PLUS environments written in 1. Bill Venables and David M.

2. User’s Guide. Автомобильная охранная система “MONGOOSE” Модель EMS 1.7R. 51. EMS 1.7R. 3. Нажмите кнопку 1 программируемого пульта. Короткий звуковой сигнал подтвердит запись кода пульта в память системы. К программе Wheel Daemon 4.0 вышло бесплатное дополнение Wheel Daemon Chains. Ознакомиться с 100 просмотров R - Matrix 1.7.

Smith when at the University of Adelaide. We. have made a number of small changes to reflect differences between the. R and S programs, and expanded some of the material. We would like to extend warm thanks to Bill Venables (and David Smith). R from way back. Comments and corrections are always welcome.

Please address email. R- core@R- project. Suggestions to the reader.

Most R novices will start with the introductory session in Appendix. A. This should give some familiarity with the style of R sessions. Many users will come to R mainly for its graphical facilities. See Graphics, which can be read at almost any time and need not wait. Introduction and preliminaries. The R environment.

R is an integrated suite of software facilities for data. Among other things it. S’). which includes conditionals, loops, user defined recursive functions and.

Indeed most of the system supplied. S language.). The term “environment” is intended to characterize it as a fully. R is very much a vehicle for newly developing methods of interactive.

It has developed rapidly, and has been extended by a. However, most programs written in. R are essentially ephemeral, written for a single piece of data. Related software and documentation. R can be regarded as an implementation of the S language which. Bell Laboratories by Rick Becker, John Chambers and.

Allan Wilks, and also forms the basis of the S- PLUS systems. The evolution of the S language is characterized by four books by. John Chambers and coauthors.

For R, the basic reference is The. New S Language: A Programming Environment for Data Analysis and. Graphics by Richard A. Becker, John M. Chambers and Allan R.

Wilks. The new features of the 1. S. are covered in Statistical Models in S edited by John M. Chambers and Trevor J. Hastie. The formal methods and classes of the. Programming. with Data by John M. Chambers. See References, for precise.

There are now a number of books which describe how to use R for data. S/S- PLUS can. typically be used with R, keeping the differences between the S. See What documentation exists for R? The R statistical system FAQ.

R and statistics. Our introduction to the R environment did not mention. R as a statistics system. We. prefer to think of it of an environment within which many classical and. A few of these are. R environment, but many are supplied as. There are about 2.

R (called. “standard” and “recommended” packages) and many more are available. CRAN family of Internet sites (via. CRAN. R- project. More details on. packages are given later (see Packages).

Most classical statistics and much of the latest methodology is. R, but users may need to be prepared to do a. There is an important difference in philosophy between S (and hence. R) and the other main statistical systems. In S a statistical.

Thus whereas SAS and SPSS will give. R will. give minimal output and store the results in a fit object for subsequent.

R functions. 1. 4 R and the window system. The most convenient way to use R is at a graphics workstation running.

This guide is aimed at users who have this. In particular we will occasionally refer to the use of R. X window system although the vast bulk of what is said applies. R environment. Most users will find it necessary to interact directly with the.

In this guide, we. UNIX machines. If you are running R under Windows or OS X you will need to make. Setting up a workstation to take full advantage of the customizable. R is a straightforward if somewhat tedious procedure, and. Users in difficulty should seek.

Using R interactively. When you use the R program it issues a prompt when it expects input. The default prompt is ‘> ’, which on UNIX might be. However, as we shall see, it is easy to change to a.

R prompt if you wish. We will assume that the UNIX shell. In using R under UNIX the suggested procedure for the first occasion. Create a separate sub- directory, say work, to hold data files on. R for this problem. This will be the working. R for this particular problem.

Start the R program with the command. At this point R commands may be issued (see later).

To quit the R program the command is. At this point you will be asked whether you want to save the data from. R session. On some systems this will bring up a dialog box, and. R session. Data which is saved will be available in future R. Further R sessions are simple. Make work the working directory and start the program as before.

Use the R program, terminating with the q() command at the end. To use R under Windows the procedure to. Create a folder as the working directory. Start In field in your R shortcut. Then launch R by double clicking on the icon. An introductory session.

Readers wishing to get a feel for R at a computer before proceeding. A sample session. Getting help with functions and features. R has an inbuilt help facility similar to the man facility of. UNIX. To get more information on any specific named function, for.

An alternative is. For a feature specified by special characters, the argument must be. This is also necessary for a few words with syntactic meaning including.

Either form of quote mark may be used to escape the other, as in the. It's important". Our convention is to use. On most R installations help is available in HTML format by. Web browser that allows the help pages to be browsed.

On UNIX, subsequent help requests are sent to the. HTML- based help system.

The ‘Search Engine and Keywords’ link in the. It can be a great way to get your bearings quickly and to. R has to offer. The help. For example. Try ? The examples on a help topic can normally be run by. Windows versions of R have other optional help systems: use. R commands, case sensitivity, etc.

Technically R is an expression language with a very simple. It is case sensitive as are most UNIX based packages, so.

A and a are different symbols and would refer to different. The set of symbols which can be used in R names depends. R is being run. (technically on the locale in use). Normally all alphanumeric. Names are effectively. Elementary commands consist of either expressions or. If an expression is given as a command, it is.

An assignment also evaluates an expression and passes the. Commands are separated either by a semi- colon (‘; ’), or by a. Elementary commands can be grouped together into one compound.

Comments can be put almost. If a command is not complete at the end of a line, R will.

This prompt may be changed by the. We will generally omit the continuation prompt. Command lines entered at the console are limited. Recall and correction of previous commands.

Under many versions of UNIX and on Windows, R provides a mechanism. The vertical arrow.

Once a command is located in this way, the. DEL key or added with the. More details are provided later: see The command- line editor. The recall and editing capabilities under UNIX are highly customizable.

You can find out how to do this by reading the manual entry for the. Alternatively, the Emacs text editor provides more general support.

ESS, Emacs Speaks Statistics) for. R. See R and Emacs in The R. FAQ. 1. 1. 0 Executing commands from or diverting output to a file. If commands. 5 are stored in an external. R in the working directory work, they. R session with the command. For Windows Source is also available on the.

File menu. The function sink. The command. restores it to the console once again.

Data permanency and removing objects. The entities that R creates and manipulates are known as.

These may be variables, arrays of numbers, character. During an R session, objects are created and stored by name (we. The R command. (alternatively, ls()) can be used to display the names of (most.

R. The collection. To remove objects the function rm is available. All objects created during an R session can be stored permanently in. R sessions. At the end of each R session. If you indicate that you want to do this, the objects are. RData. 6 in the. current directory, and the command lines used in the session are saved.

Rhistory. When R is started at later time from the same directory it reloads. At the same time the associated commands. It is recommended that you should use separate working directories for.

R. It is quite common for objects with names. Names like this. are often meaningful in the context of a single analysis, but it can be.

Simple manipulations; numbers and vectors. Vectors and assignment. R operates on named data structures. The simplest such. To set up a vector.

R command. > x < - c(1. This is an assignment statement using the functionc() which in this context can take an arbitrary number of vector. A number occurring by itself in an expression is taken as a vector of. Notice that the assignment operator (‘< -’), which consists. In most contexts the ‘=’ operator can be used as an alternative. Assignment can also be made using the function assign(). An. equivalent way of making the same assignment as above is with.

The usual operator, < -, can be thought of as a syntactic. Assignments can also be made in the other direction, using the obvious. So the same assignment could be made. If an expression is used as a complete command, the value is printed.

Программа из книги Кирсанов М. Н. Графы в Maple. Кирсанов М. Н. Графы в Maple Физматлит 2. Задача о назначениях > A: =Matrix([[1,7,1,3],[1,6,4,6],[1. Из каждой строки вычитаем min> m: =min(op(convert(Row(A,i),list))); > R[i]: =convert(map(`- `,Row(A,i),m),list); od: > A: =Matrix([seq(R[i],i=N)]): Из каждого столбца вычитаем min > m: =min(op(convert(Column(A,i),list))); > C[i]: =convert(map(`- `,Column(A,i),m),list); od: > A: =Transpose(Matrix([seq(C[i],i=N)])): > while n. Пока паросочетание не совершенное> for i to n do # Матрица A1 двудольного графа> if A[i,j]=0 then A1[i,j]: =1 else A1[i,j]: =0: fi; Считывание подпрограммы Bipart. Card из файла bipart.

Bipart. Card(A1): # Максимальное паросочестание> n. B)[3]): # Число 1 в матрице B> C: =A1- B: # Матрица графа с дугами от X к Y> for i to n do # Множества вершин, не входящих в паросочетание> if not 1 in convert(Row(B,i),list) then XM: =XM union {i} fi; > if not 1 in convert(Column(B,i),list) then YM: =YM union {i} fi; > Xs: =XM: Ys: ={}: # Множества вершин, достижимых из XM> while L1. Пока не установится процесс> if B[j,i]=1 then Xs: =Xs union {j}; fi; > sbm: =Sub.

Matrix(A,[op(Xs)],[op(Y0)]): # Подматрица из строк Xs и столбцов Y0> m: =min(op(convert(sbm,set))); > if i in Xs then A[i,j]: =A[i,j]- m; fi; > if j in Ys then A[i,j]: =A[i,j]+m; fi; > Min. Sum=add(add(B[i,j]*A0[i,j],i=N),j=N).