%\iffalse % file: linearregression.dtx % author: Battista Benciolini % contact: benciolinibattista at gmail dot com % date: see preamble % % process this file with pdflatex to obtain: % % - linearregression.pfd (full documentation, three pass needed) % - mainlinearregression.tex (interactive main-program document) % - linearregression.sty (package) % - sampledata.txt (as the name says) % % The author would strongly appreciate to receive % any comment, criticism and just usage report % %\fi %\iffalse %<*ins> \begingroup \input docstrip.tex \keepsilent \preamble ------------------------------------------------------------------------ [2024-06-10] This file is part of the (expanded) distribution of linearregression The author of linearregression is Battista Benciolini ------------------------------------------------------------------------ The author would strongly appreciate to receive any comment, criticism and just usage report ------------------------------------------------------------------------ This program may be used, distributed and modified under the conditions of the LaTeX Project Public License. (see: http://www.latex-project.org/lppl.txt) ------------------------------------------------------------------------ \endpreamble \askforoverwritefalse \generate{\file{linearregression.sty}{\from{linearregression.dtx}{package}}} \generate{\file{mainlinearregression.tex}{\from{linearregression.dtx}{main}}} \nopreamble\nopostamble \generate{\file{sampledata.txt}{\from{linearregression.dtx}{data}}} \endgroup % %\fi %\iffalse %<*driver> \documentclass[a4paper,10pt]{ltxdoc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage[lite,nobysame,non-compressed-cites]{amsrefs} \usepackage{amsmath,amssymb,amsfonts} \usepackage{multicol} \usepackage{linearregression} \usepackage{graphics} \DeclareRobustCommand*{\Ars}{\textsf{% \lower -.48ex\hbox{\rotatebox{-20}{A}}\kern -.3em{rs}}% \discretionary{-}{}{\kern -.05em}\TeX\discretionary{-}{}{% \kern -.17em}\lower -.357ex\hbox{nica}}% excerpt from some GUIT sty file \NewDocumentCommand\vect{m}{\underline{#1}} % vector \NewDocumentCommand\barycenter{m}{\overline{#1}} % barycenter \NewDocumentCommand\point{}{\vect{y}} % point \NewDocumentCommand\coeff{}{\vect{x}} % direction \NewDocumentCommand\dx{}{\vect\delta} % direction variation \NewDocumentCommand\vv{}{\vect{v}} % barycentric coordinates \NewDocumentCommand\trasp{}{^{\mathsf{T}}} % traspose \NewDocumentCommand\Renne{}{\mathbb{R}^n} % vector space \NewDocumentCommand\dor{}{f} % distance from origin \NewDocumentCommand\ipoint{}{i} % index for points \NewDocumentCommand\pointsum{}{\sum_{\ipoint=1}^m} % sum over points \NewDocumentCommand\reff{m}{(\ref{#1})} % ref in ( ) \NewDocumentCommand\matr{m}{{#1}} % matrix \NewDocumentCommand\mC{}{\matr{C}} % matrix C \NewDocumentCommand\mc{}{k} % elements of matrix C \NewDocumentCommand\mL{}{\matr{\Lambda}} % matrix lambda \NewDocumentCommand\mX{}{\matr{X}} % matrix X \NewDocumentCommand\spm{}{\phantom{-}} % space for the sign \NewDocumentCommand\ctext{}{caption} % caption (a variable !) \NewDocumentCommand\matrixtwotwo{mmmm}{ % | 2 x 2 \begin{pmatrix} #1 & #2 \\ #3 & #4 \end{pmatrix}} % | matrix \DeclareMathOperator\tr{tr} % trace \DeclareMathOperator\sgn{sgn} % signum \title{Linear regression with \LaTeX} \author{Battista Benciolini} \NewDocumentCommand\titleauthorfootnote{}{\begingroup% Not an elegant solution \let\thefootnote\relax % but it is ok at the moment \footnote{Linear regression with LaTeX - available in CTAN}% \footnote{Battista Benciolini - contact: benciolinibattista at gmail dot com}% \endgroup\setcounter{footnote}{0}}% \parindent=0pt \begin{document} \hypersetup{hidelinks} \maketitle \titleauthorfootnote \tableofcontents \vfill \DocInput{linearregression.dtx} \end{document} % %\fi % % \section{Introduction: first description of the problem\label{intro}} % I start with a quote from \Ars\ (April 2021, number 31, page 73): % \begin{quotation} % The physicist Mario Rossi is investigating a phenomenon, % presumably linear, and he performs measurements in his laboratory % to verify his hypothesis; he measures the quantity $x$ which generates % the phenomenon and he measures also one of the characteristics % $y$ showed by the phenomenon under the effect of the stimulation $x$. % \\ ... \par % Subsequently Mario graphs the data of the table to judge if the points % reasonably follow a linear trend or not; in this regard he computes the % parameters of the regression line and he draws this line on the graph % in order to judge the quality of the obtained results. % \\ ... \par % Being a \LaTeX\ user, he thinks to kill two birds with one stone: % using \LaTeX\ to draw the graph with the experimental data consisting % in the $x$, $y$ points and, at the same time, to compute the % parameter $a$ e $b$ of the regression line $y = ax+b$, % and finally to draw also this line on the same graph. % \end{quotation} % A summary description of the the problem is therefore the following. % A set of data pairs is available and each pair is represented as a point % in the plain. A straight line is searched that optimally approximates % the points. The first step is therefore the choice of an optimality criterion. % This choice is the topic of the next section. \par % From the text we also know that the possible deviation of $y$ % with respect to the model is quite larger than the uncertainty of $x$. % \par % After reading the description of the problem % of Mario Rossi I tried to produce a solution. % In this work I will use $y_1$ and $y_2$ % instead of $x$ and $y$ for the two measured quantities that % will become the first and second coordinate, or abscissa and ordinate, % in the Cartesian plane. % \par % The problem can be treated as a mere problem of approximation or % alternatively as an estimation problem in the frame of a % probabilistic description of the uncertainty. The two treatments are % conceptually different. The probabilistic treatment produces some more % results, but the estimation of the parameters is the same. % On the other hand the treatment as an approximation problem is in some sense % more immediate and requires a less extended theoretical background. % For this reason it will be preferred here. % I consider the original problem and also a variation % of it based on the assumption that the two variables are known with % the same uncertainty. The two considered situations will prove % to be quite different. % % \section{Geometric definition of there optimality criteria} % \begin{figure} % \setlength\unitlength{4cm} % \begin{picture}(1, 0.7)(0.,0.) % \multiput(0.,0.)(1.1,0){3}{\line(1,0){1}} % \multiput(0.,0.)(1.1,0){3}{\line(0,1){1}} % \multiput(1,1)(1.1,0){3}{\line(-1,0){1}} % \multiput(1,1)(1.1,0){3}{\line(0,-1){1}} % \thicklines % \multiput(0.08,0.06)(1.1,0){3}{\line(4,3){0.88}} % \multiput(0.26,0.09)(1.1,0){3}{\circle{0.03}} % \multiput(0.45,0.65)(1.1,0){3}{\circle{0.03}} % \multiput(0.92,0.44)(1.1,0){3}{\circle{0.03}} % \put(0.26,0.09){\line(0,1){0.1050}} % \put(0.45,0.65){\line(0,-1){0.3125}} % \put(0.92,0.44){\line(0,1){0.2500}} % \put(1.36,0.09){\line(-1,0){0.14}} % \put(1.55,0.65){\line( 1,0){0.4167}} % \put(2.02,0.44){\line(-1,0){0.3333}} % \put(2.46,0.09){\line(-3,4){0.05}} % \put(2.65,0.65){\line(3,-4){0.15}} % \put(3.12,0.44){\line(-3,4){0.12}} % \end{picture} % \caption{The three kinds of segments % used in the definition of the objective function} % \label{fig:criteria} % \end{figure} % For each point given in the plane we can consider the corresponding point % with the same abscissa and belonging to the line. % Remember that the line is exactly what has to be determined. % The distance between the given point and the just defined point on the line % is a reasonable measure of the discrepancy between the empirical data and the % corresponding theoretical model. % The distances we are speaking about are the length of the segments shown % in the leftmost scheme of figure (\ref{fig:criteria}). % To obtain a global discrepancy measure that considers all the points % at once we perform the sum of the squares of the lengths % of the mentioned segments. It is now clear that the two coordinates % of the points are treated quite differently and play a different role in % the definition of the optimality criterion. This choice is reasonable when % the measuring errors only (or mainly) affect the second coordinate. % The optimal line is the line that minimize the just defined % global discrepancy. The procedure for the determination of the optimal line % is named linear regression. % In this work it is named \textit{classical linear regression}. % We can easily exchange the role of the two quantities, i.e.\ we can % imagine that the first quantity is affected by errors. % The problem is not conceptually different. The segments plotted in the % central picture % of figure (\ref{fig:criteria}) represent the discrepancy between % the empirical data and the model. % This other procedure is named \textit{classical linear regression % with inverted role of the coordinates}.\par % The situation is really different if the two coordinates have to be treated % symmetrically. % In this case the discrepancy between % the empirical data and the model must be defined in a purely geometrical way. % Just the line and the points enter in the definition without any special role % for any predefined direction. With these requirements it is quite natural % to use the distance of each point from the line. % Remember that the distance of a point % from a line is intended along the shortest path, i.e.\ measured in the % direction orthogonal to the line itself. The rightmost scheme of % figure (\ref{fig:criteria}) shows the segments that are considered. % The global measure of discrepancy is again obtained as the sum % of the squares of the length of the mentioned orthogonal segments. % The procedure that obtain the optimal line that % minimize the just defined global discrepancy is named % \textit{symmetrical linear regression}.\par % Some arguments of the present section will be repeated in section % \ref{package} from the algebraic and computational point of view. % % \section{General information on the proposed solution, including limitations} % The code that implements the solution is recorded in two files, that are % a package (sty) file and a main interactive document. % The file |linearregression.sty| provides several commands % that can be used in any document. The file |mainlinearregression.tex| % provides a simple interactive user interface. % The package described in the sections \ref{manual} and % \ref{package} (user manual and implementation) provides the % functions that execute the various needed operations, i.e.\ % data input, computations, printing the numerical results and % generating a graphic representation of data and results. % Some auxiliary functions complete the package. % The design of the output (tables and plots) includes some arbitrary choices. % The style of the graphic output is quite minimalist % (e.g.:\ no colors, no variations of line styles).\par % % \section{Some comments about the programming aspect of the package % and its documentation} % Large part of the code is written using the |expl3| language. % (Is it also named simply L3 ? Does expl still means experimental ?) % I have tried to be compliant with the various recommendations and % prescriptions for a correct use of the language, % but I probably only partly succeeded.\par % Different more elegant and more coherent solutions probably exist % both for the general structure of the package and for some specific part % of the code, but this is what I have been able to do. % Some perhaps problematic aspects are mentioned here after\par % Several used variables are global and they are accessed by various functions. % This makes the various parts of the package % quite connected to each other and creates strong dependencies. \par % The layered programming style is only partially applied. % The partition between document command and lower level functions is present, % but part of the low level code is directly in the document commands. % Variants are not used.\par % One more remarks concern the documentation. % I was uncertain about the opportunity of using the class |l3doc|. I decided to % remain using |ltxdoc|. This is the reason why I do not use the environment % |macro| and the command |\cs| in the documentation of some auxiliary % functions named according with the |expl3| standard. % (I have just an interim far from optimal solution % for a reasonable formatting.) % % \section{A ready to use simple user interface\label{main}} % The main file asks the user for the name of a % file containing the data and generates a one (or two) page output. %\iffalse %<*main> %\fi % \begin{macrocode} \documentclass[a4paper]{article} \usepackage{lmodern} \usepackage{linearregression} \begin{document} \pagestyle{empty} \lraskfilename \lrcomputation \lrplot{12.0}{+}{+}{-}{-} \lrprint \end{document} % \end{macrocode} %\iffalse % %\fi % % \section{A user manual for the package\label{manual}} % The various analysis of a data set and the representation of the data % and of the results is obtained with a sequence of several commands. % The main operations are: % (i) selection of the data file, (ii) data imput and computation, % (iii) printing of a table, % (iv) printing of a picture (that can be repeated with different parameters). % It is generally convenient to put the table and the picture(s) % in a proper floating environment. % The commands for the four mentioned operations are described here after. % The first needed operation is to set the name of the data file. % This is done with the command \DescribeMacro{\lrfilename} % \cs{lrfilename}\marg{file} that has a mandatory argument. % The argument is the name of the data file. As an alternative the % command \DescribeMacro{\lraskfilename} \cs{lraskfilename} can be used. % It asks the user to type the name of the data file in the terminal. % \par % The macro \DescribeMacro{\lrcomputation} % \cs{lrcomputation} reads the data % and performs all the computations. % The results of the computations remain available in internal % variables and are then used by the macro that print them % or generates a plot. %\par % The macro \DescribeMacro{\lrprint} % \cs{lrprint} generates a table with all the estimated % parameters and some information about the data. % \par % The macro \DescribeMacro{\lrplot} % \cs{lrplot}\marg{imagewidth}\marg{key1}\marg{key2}\marg{key3}\marg{key4} % really generates the plot. The first argument is the % width of the plot, while the height is computed according % to the distribution of the points. The other four arguments are referred % to the data points, to the lines determined with classical regression, % with classical regression with inverted role of the coordinates and % with symmetric regression. % The four items, i.e.\ the set of points and the three lines, are drawn % or not according to the corresponding character found in |key|$i$. % Each item is not plotted if the character is a |-|, it is plotted in any other % case. Furthermore the lines are accompanied by a label made by the % corresponding |key|, unless it is just a |+|. % \par % Few words are necessary about the format of the data file. % Each record of the file hold the two values related to a point. % The two values must be separated by any number (one is needed as a minimum) of % space and comma characters. No character different from space % can be accepted before the first value and after the second value. % % \section{An example\label{example}} % The data reported here after will be available in |sampledata.txt| % and will be used in the example presented in this section . %\iffalse %<*data> %\fi % \begin{multicols}{4} % \begin{macrocode} -0.546 0.107 1.093 -0.510 1.440 1.995 1.414 0.991 0.735 1.585 -1.848 -0.235 -0.203 -0.292 1.517 0.779 0.559 -1.341 -0.462 -0.437 -0.785 -0.661 -0.558 0.397 0.181 -2.616 0.619 1.859 -0.223 -1.915 0.629 -0.534 -1.989 -2.300 -0.241 1.098 -0.931 -1.613 -1.070 0.592 2.341 0.413 1.993 -0.111 -2.357 -0.312 -1.975 0.140 % \end{macrocode} % \end{multicols} %\iffalse % %\fi % % The analysis of the sample data and the generation of a numeric table % is operated by a code similar to the following % (see table \ref{tab:sampledata}). \\ % |\lrfilename{sampledata.txt}| \\ |\lrcomputation| \\ % |\begin{table}| \\ % | \lrprint| \\ % | \caption{Analysis of ... }| \\ |\label{tab:sampledata}\end{table}| % \par % The generation of some different graphical representation of the data and of % the results is operated by a code similar to the following % (see figures \ref{fig:sampledataB} ).\\ % \RenewDocumentCommand\ctext{}{LEFT The three lines are obtained with the three % optimality criteria. (AA) classical linear regression; (BB) classical linear % regression with inverted role of the coordinates; (S) symmetric linear % regression. RIGHT Data points and line estimated with % symmetric linear regression.} % |\begin{figure}|\\|\lrplot{10.}{-}{AA}{BB}{S}| \\ % |\lrplot{10.}{+}{-}{-}{+}| % \\ |\caption{|\ctext|}|\\ | \label{fig:sampledataB} \end{figure}| % % \lrfilename{sampledata.txt} \lrcomputation % \begin{table} \lrprint \caption{Analysis of the sample data} % \label{tab:sampledata} \end{table} % \begin{figure} \lrplot{6.}{-}{AA}{BB}{S} \hfill \lrplot{6.}{+}{-}{-}{+} % \caption{\ctext} \label{fig:sampledataB} \end{figure} % % \section{A package for linear regression % and the theory behind it\label{package}} %\iffalse %<*package> %\fi % % \subsection{Math preliminaries and notation \label{prelim}} % The coordinates of a set of $m$ points on the plane are available. % A straight line is searched that optimally approximates the points.\par % The coordinates of a generic point are $y_1$ and $y_2$ % and they are collected in the vector $\point$. % Any given point is identified with the index $\ipoint$. % (Explicit indices $(\dots)_1$ or $(\dots)_2$ always refer to the first % or second coordinate of a point or to the first or second component % of a vector in the plane. % Symbolic index $(\dots)\ipoint$ always refers to the different points. Few % formulas require both indices $(\dots)_{1\ipoint}$, $(\dots)_{2\ipoint}$.)\par % With more then two points a criterion of best approximation % is needed to select the optimal line that describes the data. \par % Lower case symbols are used for scalars. Lower case underlined % symbols are used for vectors in the plane. Upper case symbols % are used for matrices. % \par % It is possible that certain data generate an ambiguity or a singularity % in the computation. % The following mathematical treatment of the problem % do not mention these situations and the code does not deal with them. % % \subsection{Package declaration, required package and definition of variables} % The various macro will be provided in a package file % that is introduced as usual. Most of the macros require % the \LaTeX3 syntax. % \begin{macrocode} \ProvidesPackage{linearregression}[2024-06-10] \RequirePackage{pict2e} \ExplSyntaxOn % \end{macrocode} % The variables used in the package are defined hereafter. % \begin{macrocode} \ior_new:N \g_BBLR_file_ior \tl_new:N \g_BBLR_file_name_tl \int_new:N \g_BBLR_number_of_points_int \fp_new:N \g_BBLR_abscissa_fp \fp_new:N \g_BBLR_ordinate_fp \fp_new:N \g_BBLR_mean_abscissa_fp \fp_new:N \g_BBLR_mean_ordinate_fp \fp_new:N \g_BBLR_abscissa_SecOrdMoment_fp \fp_new:N \g_BBLR_ordinate_SecOrdMoment_fp \fp_new:N \g_BBLR_mixed_SecOrdMoment_fp \fp_new:N \g_BBLR_slope_A_fp \fp_new:N \g_BBLR_slope_B_fp \fp_new:N \g_BBLR_slope_S_fp \fp_new:N \g_BBLR_intercept_A_fp \fp_new:N \g_BBLR_intercept_B_fp \fp_new:N \g_BBLR_intercept_S_fp \fp_new:N \g_BBLR_cos_fp \fp_new:N \g_BBLR_sin_fp \fp_new:N \g_BBLR_sig_sin_fp \fp_new:N \g_BBLR_eig_diff_fp \fp_new:N \g_BBLR_diag_diff_fp \tl_new:N \g_BBLR_file_line_tl \fp_new:N \g_BBLR_min_abscissa_fp \fp_new:N \g_BBLR_min_ordinate_fp \fp_new:N \g_BBLR_max_abscissa_fp \fp_new:N \g_BBLR_max_ordinate_fp \fp_new:N \g_BBLR_min_draw_abscissa_fp \fp_new:N \g_BBLR_max_draw_abscissa_fp \bool_new:N \g_BBLR_data_eof_bool \int_new:N \g_BBLR_record_length_int \int_new:N \g_BBLR_rec_count_int \int_new:N \g_BBLR_first_separator_int \int_new:N \g_BBLR_last_separator_int \str_const:Nn \c_BBLR_space_str {~} \str_const:Nn \c_BBLR_comma_str {,} \str_const:Nn \c_BBLR_plus_str {+} \str_const:Nn \c_BBLR_minus_str {-} \bool_new:N \g_BBLR_plot_points_bool \bool_new:N \g_BBLR_plot_lineA_bool \bool_new:N \g_BBLR_plot_lineB_bool \bool_new:N \g_BBLR_plot_lineS_bool \fp_new:N \g_BBLR_base_fp \fp_new:N \g_BBLR_height_fp \fp_new:N \g_BBLR_Xbase_fp \fp_new:N \g_BBLR_Xheight_fp \fp_new:N \g_BBLR_Dabscissa_fp \fp_new:N \g_BBLR_Dordinate_fp \fp_new:N \g_BBLR_diameter_fp \fp_gset:Nn \g_BBLR_diameter_fp{0.2} \fp_new:N \g_BBLR_line_base_length_fp \fp_new:N \g_BBLR_scale_factor_fp \str_new:N \c_BBLR_point_code_str \str_new:N \g_BBLR_labelA_str \str_new:N \g_BBLR_labelB_str \str_new:N \g_BBLR_labelS_str % \end{macrocode} % % \subsection{Preparing data input} % \begin{macro}{\lrfilename} % The command \cs{lrfilename} records the file name passed as argument. % \begin{macrocode} \NewDocumentCommand{\lrfilename}{m}{ \tl_gset:Nn \g_BBLR_file_name_tl {#1} } % \end{macrocode} % \end{macro} % \begin{macro}{\lraskfilename} % The command \cs{lraskfilename} asks for the data file name from the terminal. % \begin{macrocode} \NewDocumentCommand{\lraskfilename}{}{ \ior_get_term:nN {filename ? } \g_BBLR_file_name_tl \tl_trim_spaces:N \g_BBLR_file_name_tl } % \end{macrocode} % \end{macro} % % \subsection{Main command declaration, computation of % first and second order moments} % \begin{macro}{\lrcomputation} % The command \cs{lrcomputation} reads the data file and % performs all the relevant computations to solve the % proposed problem. % \begin{macrocode} \NewDocumentCommand{\lrcomputation}{}{% % \end{macrocode} % % In the sequel it will results that the first and second order moments % of the data provide everything needed to solve the problem. % The barycenter of the data is defined as % \begin{equation} % \barycenter{\point}=\frac{1}{m}\pointsum \point_\ipoint. % \label{barycenter} \end{equation} % It is convenient to scan the data to accumulate the sum % that appears in \reff{barycenter}. % The coordinates of each point are read from the file % and they are immediately used. % It is therefore not necessary to globally record the data. % \begin{macrocode} \bool_gset_false:N \g_BBLR_data_eof_bool \int_zero:N \g_BBLR_number_of_points_int \fp_zero:N \g_BBLR_mean_abscissa_fp \fp_zero:N \g_BBLR_mean_ordinate_fp \ior_open:Nn \g_BBLR_file_ior \g_BBLR_file_name_tl \bool_until_do:Nn \g_BBLR_data_eof_bool { \ior_str_get:NN \g_BBLR_file_ior \g_BBLR_file_line_tl \if_eof:w \g_BBLR_file_ior \bool_gset_true:N \g_BBLR_data_eof_bool \else: \int_incr:N \g_BBLR_number_of_points_int \BBLR_decode_data: \fp_gset:Nn \g_BBLR_mean_abscissa_fp {\g_BBLR_mean_abscissa_fp + \g_BBLR_abscissa_fp} \fp_gset:Nn \g_BBLR_mean_ordinate_fp {\g_BBLR_mean_ordinate_fp + \g_BBLR_ordinate_fp} \fi: } % \end{macrocode} % Loop ended. Now close the file and divide by the number of points. % \begin{macrocode} \ior_close:N \g_BBLR_file_ior \fp_gset:Nn \g_BBLR_mean_abscissa_fp {\g_BBLR_mean_abscissa_fp / \g_BBLR_number_of_points_int} \fp_gset:Nn \g_BBLR_mean_ordinate_fp {\g_BBLR_mean_ordinate_fp / \g_BBLR_number_of_points_int} % \end{macrocode} % % The barycentric coordinates are defined for each point % \begin{equation} \vv_\ipoint= \point_\ipoint - \barycenter{\point} % \label{residual} \end{equation} % and the empirical dispersion matrix is defined as: % \begin{equation} \mC=\frac{1}{m}\pointsum \vv_\ipoint\vv_\ipoint\trasp . % \label{matrixC} \end{equation} % Superscript as in $()\trasp$ means transpose. The elements of $\mC$ are the % second order central moments and they are denoted as: % \begin{equation} \mC=\matrixtwotwo{\mc_{11}}{\mc_{12}}{\mc_{12}}{\mc_{22}}. % \label{matrixCc} \end{equation} % A second scan of the data is performed to compute the % sums that appears in \reff{matrixC} and to determine the % the extremal values of the coordinates. Record scan can be regulated % by a record counter, because the the number of points is now known. % \begin{macrocode} \fp_zero:N \g_BBLR_abscissa_SecOrdMoment_fp \fp_zero:N \g_BBLR_ordinate_SecOrdMoment_fp \fp_zero:N \g_BBLR_mixed_SecOrdMoment_fp \fp_gset_eq:NN \g_BBLR_min_abscissa_fp \g_BBLR_mean_abscissa_fp \fp_gset_eq:NN \g_BBLR_min_ordinate_fp \g_BBLR_mean_ordinate_fp \fp_gset_eq:NN \g_BBLR_max_abscissa_fp \g_BBLR_mean_abscissa_fp \fp_gset_eq:NN \g_BBLR_max_ordinate_fp \g_BBLR_mean_ordinate_fp \ior_open:Nn \g_BBLR_file_ior \g_BBLR_file_name_tl \int_zero:N \g_BBLR_rec_count_int \int_do_until:nn {\g_BBLR_rec_count_int = \g_BBLR_number_of_points_int} { \ior_str_get:NN \g_BBLR_file_ior \g_BBLR_file_line_tl \int_incr:N \g_BBLR_rec_count_int \BBLR_decode_data: \fp_gset:Nn \g_tmpa_fp {\g_BBLR_abscissa_fp - \g_BBLR_mean_abscissa_fp} \fp_gset:Nn \g_tmpb_fp {\g_BBLR_ordinate_fp - \g_BBLR_mean_ordinate_fp} \fp_gset:Nn \g_BBLR_abscissa_SecOrdMoment_fp {\g_BBLR_abscissa_SecOrdMoment_fp + \g_tmpa_fp * \g_tmpa_fp} \fp_gset:Nn \g_BBLR_mixed_SecOrdMoment_fp {\g_BBLR_mixed_SecOrdMoment_fp + \g_tmpa_fp * \g_tmpb_fp} \fp_gset:Nn \g_BBLR_ordinate_SecOrdMoment_fp {\g_BBLR_ordinate_SecOrdMoment_fp + \g_tmpb_fp * \g_tmpb_fp} \fp_gset:Nn \g_BBLR_min_abscissa_fp {min(\g_BBLR_min_abscissa_fp, \g_BBLR_abscissa_fp)} \fp_gset:Nn \g_BBLR_min_ordinate_fp {min(\g_BBLR_min_ordinate_fp, \g_BBLR_ordinate_fp)} \fp_gset:Nn \g_BBLR_max_abscissa_fp {max(\g_BBLR_max_abscissa_fp, \g_BBLR_abscissa_fp)} \fp_gset:Nn \g_BBLR_max_ordinate_fp {max(\g_BBLR_max_ordinate_fp, \g_BBLR_ordinate_fp)} } \ior_close:N \g_BBLR_file_ior \fp_gset:Nn \g_BBLR_abscissa_SecOrdMoment_fp {\g_BBLR_abscissa_SecOrdMoment_fp / \g_BBLR_number_of_points_int} \fp_gset:Nn \g_BBLR_mixed_SecOrdMoment_fp {\g_BBLR_mixed_SecOrdMoment_fp / \g_BBLR_number_of_points_int} \fp_gset:Nn \g_BBLR_ordinate_SecOrdMoment_fp {\g_BBLR_ordinate_SecOrdMoment_fp / \g_BBLR_number_of_points_int} \fp_gset:Nn \g_BBLR_Dabscissa_fp {\g_BBLR_max_abscissa_fp - \g_BBLR_min_abscissa_fp } \fp_gset:Nn \g_BBLR_Dordinate_fp {\g_BBLR_max_ordinate_fp - \g_BBLR_min_ordinate_fp } % \end{macrocode} % A single pass algorithm exists, but it is numerically less stable. % % \subsection{Classical linear regression \label{classical}} % A line in the plane is described by the equation % \begin{equation} y_2=ay_1+b \label{eqab} \end{equation} % that contains the parameters $a$ and $b$. % For each point it is possible to define a distance or a discrepancy % of the experimental data with respect to the model. % In the given problem the second coordinate is much more affected by % errors than the first coordinate. It is therefore reasonable % to define the approximation error of each point as % \begin{equation} e_\ipoint=y_{2\ipoint}-ay_{1\ipoint}-b % \label{e}\end{equation} % i.e.\ the difference between the empirical value $y_{2\ipoint}$ % and its model counterpart $ay_{1\ipoint}+b$. % The global discrepancy between the data and the model is measured by the % least square objective function defined by: % \begin{equation} \psi=\pointsum e_\ipoint^2 \label{psiab} \end{equation} % and the parameters $a$ and $b$ will be determined % just by the minimization of the function $\psi$ defined in \reff{psiab}. % \par % In the present treatment of the regression problem as a pure % approximation problem the definition of $\psi$ in \reff{psiab} % seams quite arbitrary. It is anyway a convenient choice. % \par % Expression \reff{e} can be rewritten in the different form % \begin{equation} % e_\ipoint=v_{2\ipoint}-av_{1\ipoint}+\barycenter{y}_2-a\barycenter{y}_1-b % \label{e2}\end{equation} % so that the function to be minimized can be expressed % as the sum of two quadratic functions: % \begin{equation} % \psi= % \pointsum (v_{2\ipoint}-av_{1\ipoint})^2+ % m(\barycenter{y}_2-a\barycenter{y}_1-b)^2 % \label{psiab2} \end{equation} % and the minimum can be attained considering % the two terms one at a time. % The second term in the right-hand side of \reff{psiab2} % vanishes if the choice of $b$ is: % \begin{equation} b=\barycenter{y}_2-a\barycenter{y}_1. % \label{estb} \end{equation} % The first term in the right-hand side of \reff{psiab2} becomes: % \begin{equation} \psi_{(a)}=m\left(\mc_{22}-2a\mc_{12}+a^2\mc_{11}\right). % \label{parabola} \end{equation} % Searching the minimum of $\psi$ w.r.t.\ $a$ is therefore the search % of the abscissa of the vertex of a parabola % with axis parallel to the second coordinated axis. % The result is: % \begin{equation} a=\mc_{12}/\mc_{11} % \label{esta} \end{equation} % Now the slope $a$ and the intercept $b$ can be actually computed. % \begin{macrocode} \fp_gset:Nn \g_BBLR_slope_A_fp {\g_BBLR_mixed_SecOrdMoment_fp / \g_BBLR_abscissa_SecOrdMoment_fp } \fp_gset:Nn \g_BBLR_intercept_A_fp {\g_BBLR_mean_ordinate_fp - \g_BBLR_slope_A_fp * \g_BBLR_mean_abscissa_fp} % \end{macrocode} % \par % The empirical data and the estimated values of $a$ and $b$ % can be used to compute % the value actually attained by the residuals $e_\ipoint$ and % by the function $\psi$. Then the index % \begin{equation} \hat\sigma_0^2=\psi/(m-2)\end{equation} % can be used to evaluate the general quality of the data and of the model. % This claim is clearly quite generic. A complete understanding % of this evaluation would require to treat the linear regression % problem in the framework of the probabilistic estimation theory. % The used notation is derived from that theory.\par % If the role of the two coordinates is exchanged the result % for $a$ becomes (still with reference to \reff{eqab}) % \begin{equation} a=\mc_{22}/\mc_{12}.\end{equation} % A complete treatment of this different situation would include % the redefinition of $e_\ipoint$ and of $\psi$. % The slope and the intercept can be computed according with % the different assumption. % \begin{macrocode} \fp_gset:Nn \g_BBLR_slope_B_fp {\g_BBLR_ordinate_SecOrdMoment_fp / \g_BBLR_mixed_SecOrdMoment_fp} \fp_gset:Nn \g_BBLR_intercept_B_fp {\g_BBLR_mean_ordinate_fp - \g_BBLR_slope_B_fp * \g_BBLR_mean_abscissa_fp} % \end{macrocode} % % \subsection{Symmetric linear regression \label{symmetric}} % If both the coordinates of the experimental points are affected % by the same uncertainty it is advisable to use a more symmetric % optimality criterion and it is convenient to use a different model equation. % \par % The same line can be described by a different equation, i.e.\ % \begin{equation} x_1y_1+x_2y_2=\dor \end{equation} % or in vector form: % \begin{equation} \coeff\trasp\point=\dor. \label{eqvx} \end{equation} % The parameters in \reff{eqvx} % are the scalar $\dor$ and the elements % of the vector $\coeff$, i.e.\ $x_1$ and $x_2$. % The line described by \reff{eqvx} is obviously % invariant when the three parameters are simultaneously % scaled by a constant. The normalization condition % \begin{equation} \coeff\trasp\coeff=1, \label{norm} \end{equation} % supplemented by $\dor\ge 0$, % is quite convenient because the parameters will assume % a significant geometrical meaning: % $\coeff$ is the unit vector orthogonal to the line and $\dor$ % is the distance of the line from the origin. % The expression % \begin{equation} d=\dor-\coeff\trasp\point \label{distance} \end{equation} % is the distance of the generic point $\point$ from the line % with a sign that is positive for points on the same side of the origin. % \par % The distance of each given point from the desired optimal line % is denoted by $d_\ipoint$. % It has a clear intrinsic geometrical meaning and it does not % privileges one coordinate w.r.t.\ the other. % The function to be minimized by the optimal line is % \begin{equation} % \phi=\frac{1}{m}\pointsum d_\ipoint^2. \label{phi1} \end{equation} % The parameters of \reff{eqvx} are determined by the minimization % of the function $\phi$ that can be expressed as: % \begin{equation} % \phi=\frac{1}{m}\pointsum (\coeff\trasp\point_{\ipoint}-\dor)^{2} % \label{phi2} \end{equation} % and then, after some algebraic manipulations: % \begin{equation} % \phi=\coeff\trasp\mC\coeff+(\dor-\coeff\trasp\barycenter{\point})^2 % \label{phi3}. \end{equation} % The function $\phi$ is composed (as it was the function $\psi$) by the sum % of two parts. The second term in the right-hand side of \reff{phi3} % vanishes if the choice of $\dor$ is: % \begin{equation} \dor=\coeff\trasp\barycenter{\point}. % \label{estd} \end{equation} % Then it is necessary to minimize the function % \begin{equation} \phi_{(\coeff)} = \coeff\trasp\mC\coeff % \label{quadraticfun} \end{equation} % with the constrain $\coeff\trasp\coeff=1$. % It can be proved that the function $\phi_{(\coeff)}$ % is stationary if $\coeff$ is an eigenvector of \mC. \par % The function $\phi_{(\coeff)}$ and the constrain must be combined % using a Lagrange multiplier: % \begin{equation} % \Phi= \coeff\trasp\mC\coeff+\lambda(1-\coeff\trasp\coeff). % \label{Phi} \end{equation} % Then the stationarity points of $\Phi$ must be determined. % Equating to zero the derivatives of $\Phi$ gives % \begin{equation} % \mC\coeff=\lambda\coeff % \label{auto} \end{equation} % i.e.\ $\coeff$ is an eigenvector of $\mC$. \par % The same result is obtained with the following argument. % The function $\phi_{(\coeff)}$ is stationary if its first variation % is zero. The variation of $\coeff$ is named $\dx$ . % It must respect the constrain, that becomes $\dx\trasp\coeff=0$. % The first variation of $\phi_{(\coeff)}$ is $2\dx\trasp\mC\coeff$, % and it is zero if and only if the following implication is valid: % $\dx\trasp\coeff=0 \implies \dx\trasp\mC\coeff=0$, % and the implication is valid if and only if the vector % $\mC\coeff$ has the same direction of $\coeff$, i.e.\ if % $\coeff$ is an eigenvector of $\mC$. % \par % The result on the optimal line % can be described geometrically in the following way: % (i) the optimal line includes the barycenter of the data; % (ii) the optimal line is orthogonal to the eigenvector of % $\mC$ corresponding to the minimum eigenvalue.\par % The obtained result is also valid in $\Renne$. % A set of points in $\Renne$ must be approximated by an $(n-1)$-dimensional % affine subspace. (Other more general situations can be considered.) % \par % The trace of the matrix $\mC$, denoted as $\tr(\mC)$, is a measure of the % global dispersion of the set of points. % The minimum eigenvalue $\lambda_{\textrm{min}}$ of $\mC$ is a measure % of the dispersion of the set of points with % respect to the optimal affine subspace. Therefore the index % \begin{equation} \frac{n\lambda_{\textrm{min}}}{\tr(\mC)} % \end{equation} % can be used as an indicator of the relative residual % dispersion of the data around the optimal line. % The defined index is dimensionless and it is % always between $0$ and $1$. % \par % For the actual computation of $\coeff$ it is convenient to consider % the spectral factorization of the matrix $\mC$, i.e.\ % $\mC=\mX\mL\mX\trasp$ where $\mL$ is a diagonal matrix % whose diagonal elements are the eigenvalues of $\mC$ % and $\mX$ is an orthonormal matrix whose columns are % the eigenvectors of $\mC$. The spectral factorization exists % for any symmetric matrix, but it is specially simple for % a $2\times 2$ matrix. % \begin{equation} % \matrixtwotwo{\mc_{11}}{\mc_{12}}{\mc_{12}}{\mc_{22}}= % \matrixtwotwo{c}{-s}{s}{\spm c} % \matrixtwotwo{\lambda_1}{0}{0}{\lambda_2} % \matrixtwotwo{\spm c}{s}{-s}{c} % \label{spectral}\end{equation} % The eigenvalues can be easily obtained because % their sum is the trace of $\mC$ % \begin{equation} % \lambda_1 + \lambda_2 = \mc_{11}+\mc_{22} % \label{Sum}\end{equation} % and their product % is the determinant of the same matrix. % Therefore after some manipulations it results: % \begin{equation} % \lambda_1 - \lambda_2 = \sqrt{(\mc_{11}-\mc_{22})^2+4\mc_{12}^2} % \label{Difference}\end{equation} % and the two eigenvalues are then immediately obtained. \par % It is convenient to compute the difference of the two diagonal elements % of the dispersion matrix and the difference of its eigenvalues. % \begin{macrocode} \fp_gset:Nn \g_BBLR_diag_diff_fp {\g_BBLR_abscissa_SecOrdMoment_fp - \g_BBLR_ordinate_SecOrdMoment_fp} \fp_gset:Nn \g_BBLR_eig_diff_fp {sqrt(\g_BBLR_diag_diff_fp * \g_BBLR_diag_diff_fp + 4 * \g_BBLR_mixed_SecOrdMoment_fp * \g_BBLR_mixed_SecOrdMoment_fp)} % \end{macrocode} % The computation of $c$ and $s$ is obtained from \reff{spectral} % taking into account that $c^2+s^2=1$. % From \reff{spectral} it results: % \begin{equation} \mc_{11}-\mc_{22}=(\lambda_1-\lambda_2)(c^2-s^2) % \label{Cos2A}\end{equation} % and also % \begin{equation} \mc_{12}=(\lambda_1-\lambda_2)cs % \label{Sin2A}\end{equation} % that is only used to determine the sign of $cs$. % The expression for the parameters $c$ and $s$ are: % \begin{equation} % c=\sqrt{\frac{1}{2}+\frac{\mc_{11}-\mc_{22}}{2(\lambda_1-\lambda_2)}} % \label{cos}\end{equation} % \begin{equation} % s=\sgn(\mc_{12}) % \sqrt{\frac{1}{2}-\frac{\mc_{11}-\mc_{22}}{2(\lambda_1-\lambda_2)}} % \label{sin}\end{equation} % The parameters $s$ and $c$ are the sine and cosine % of the angle between the axis of $y_1$ and the eigenvector % corresponding to the maximum eigenvalue. \par % They are computed using the already defined elements. % \begin{macrocode} \fp_gset:Nn \g_BBLR_cos_fp% {sqrt((1 + \g_BBLR_diag_diff_fp / \g_BBLR_eig_diff_fp) / 2)} \fp_gset:Nn \g_BBLR_sig_sin_fp {\fp_sign:n {\g_BBLR_mixed_SecOrdMoment_fp}} \fp_gset:Nn \g_BBLR_sin_fp {\g_BBLR_sig_sin_fp*sqrt((1-\g_BBLR_diag_diff_fp / \g_BBLR_eig_diff_fp) / 2)} % \end{macrocode} % The vector $\coeff$ is : % \begin{equation} % \coeff=\sgn(-s\barycenter{y}_1+c\barycenter{y}_2) % \begin{pmatrix} -s \\ c\end{pmatrix}. % \label{xhat}\end{equation} % %\par % The parameter $a$ of model \reff{eqab} can be obtained as: % \begin{equation} % a=s/c % \end{equation} % Now the slope and the intercept of the optimal line corresponding to the % symmetric criterion can be computed. % % \begin{macrocode} \fp_gset:Nn \g_BBLR_slope_S_fp {\g_BBLR_sin_fp / \g_BBLR_cos_fp } \fp_gset:Nn \g_BBLR_intercept_S_fp {\g_BBLR_mean_ordinate_fp - \g_BBLR_slope_S_fp * \g_BBLR_mean_abscissa_fp} } % \end{macrocode} % % The theoretical treatment of the proposed problem and the % implementation of its numerical solution end here. % \end{macro} % % \subsection{Print of table of results} % \begin{macro}{\lrprint} % The command \cs{lrprint} prints some info on the data % and the results of the computations in tabular form. % \begin{macrocode} \NewDocumentCommand{\lrprint}{}{ \begin{center} \begin{tabular}{| l | r |} \hline Data~File: & \g_BBLR_file_name_tl \\ \hline Number~of~points: & \int_use:N\g_BBLR_number_of_points_int \\ \hline Mean~values~of~the~coordinates: &% $\fp_use:N \g_BBLR_mean_abscissa_fp$ \\ & $\fp_use:N \g_BBLR_mean_ordinate_fp$ \\ \hline Minimum~values~of~the~coordinates: &% $\fp_use:N \g_BBLR_min_abscissa_fp$ \\ & $\fp_use:N \g_BBLR_min_ordinate_fp$ \\ \hline Maximum~values~of~the~coordinates: &% $\fp_use:N \g_BBLR_max_abscissa_fp$ \\ & $\fp_use:N \g_BBLR_max_ordinate_fp$ \\ \hline {Second~order~moments}\phantom{xxxxxxxxx}{abscissa} &% $\fp_use:N \g_BBLR_abscissa_SecOrdMoment_fp$ \\ \multicolumn{1}{|r|}{mixed} & % $\fp_use:N \g_BBLR_mixed_SecOrdMoment_fp$ ~ \\ \multicolumn{1}{|r|}{ordinate} & % $\fp_use:N \g_BBLR_ordinate_SecOrdMoment_fp$ \\ \hline Slope~and~intercept~of~optimal~line & $\fp_use:N \g_BBLR_slope_A_fp$ \\ (estimated~with~errors~in~ordinate)&$\fp_use:N \g_BBLR_intercept_A_fp$\\ \hline Slope~and~intercept~of~optimal~line & $\fp_use:N \g_BBLR_slope_B_fp$ \\ (estimated~with~errors~in~abscissa)&$\fp_use:N \g_BBLR_intercept_B_fp$\\ \hline Components~of~unit~vector~along~the~line & $\fp_use:N \g_BBLR_cos_fp$ \\ & $\fp_use:N \g_BBLR_sin_fp$ \\ Slope~and~intercept~of~optimal~line &$\fp_use:N \g_BBLR_slope_S_fp$ \\ (estimated~with~symmetric~regression) & $\fp_use:N \g_BBLR_intercept_S_fp$\\ \hline \end{tabular} \end{center} } % \end{macrocode} % \end{macro} % % \subsection{Plot of points and lines} % \begin{macro}{\lrplot} % The command \cs{lrplot} produce a framed plot of the data % and of the regression line(s). The size of the plot and its actual % content are determined by the arguments. % \begin{macrocode} \NewDocumentCommand{\lrplot}{mmmmm}{% % \end{macrocode} % The plotting area is divided into a main plotting area for % the representation of points and line(s) and a small surrounding free space. % The height is computed taking into account the distribution of the points. % \begin{macrocode} \fp_gset:Nn \g_BBLR_base_fp {#1} \fp_gset:Nn \g_BBLR_Xbase_fp {\g_BBLR_base_fp - 0.6} \fp_gset:Nn \g_BBLR_scale_factor_fp{\g_BBLR_Xbase_fp / \g_BBLR_Dabscissa_fp} \fp_gset:Nn \g_BBLR_Xheight_fp {\g_BBLR_Dordinate_fp * \g_BBLR_scale_factor_fp} \fp_gset:Nn \g_BBLR_height_fp {\g_BBLR_Xheight_fp + 0.6} % \end{macrocode} % The information about the items to be plotted is in the remaining arguments. % \begin{macrocode} \str_gset:Nn \g_BBLR_point_code_str {#2} \str_gset:Nn \g_BBLR_labelA_str {#3} \str_gset:Nn \g_BBLR_labelB_str {#4} \str_gset:Nn \g_BBLR_labelS_str {#5} \bool_gset:Nn \g_BBLR_plot_points_bool {!(\str_if_eq_p:NN \g_BBLR_point_code_str \c_BBLR_minus_str)} \bool_gset:Nn \g_BBLR_plot_lineA_bool {!(\str_if_eq_p:NN \g_BBLR_labelA_str \c_BBLR_minus_str)} \bool_gset:Nn \g_BBLR_plot_lineB_bool {!(\str_if_eq_p:NN \g_BBLR_labelB_str \c_BBLR_minus_str)} \bool_gset:Nn \g_BBLR_plot_lineS_bool {!(\str_if_eq_p:NN \g_BBLR_labelS_str \c_BBLR_minus_str)} % \end{macrocode} % The unit of length is $1$ centimeter. The plotting area is framed. % \begin{macrocode} \setlength{\unitlength}{1.0cm} \fp_gset:Nn \g_tmpa_fp {\g_BBLR_Xbase_fp +0.2} \fp_gset:Nn \g_tmpb_fp {\g_BBLR_Xheight_fp +0.1} \begin{picture}(\fp_use:N\g_BBLR_base_fp,\fp_use:N\g_BBLR_height_fp)(-0.3,-0.3) \put(-0.1,-0.1){\line(1,0){\fp_use:N\g_tmpa_fp}} \put(-0.1,\fp_use:N\g_tmpb_fp){\line(1,0){\fp_use:N\g_tmpa_fp}} \fp_gset:Nn \g_tmpa_fp {\g_tmpa_fp -0.1} \fp_gset:Nn \g_tmpb_fp {\g_tmpb_fp +0.1} \put(-0.1,-0.1){\line(0,1){\fp_use:N\g_tmpb_fp}} \put(\fp_use:N\g_tmpa_fp,-0.1){\line(0,1){\fp_use:N\g_tmpb_fp}} % \end{macrocode} % The plot of points and line(s) is obtained using auxiliary functions. % \begin{macrocode} \thicklines \bool_if:nT {\g_BBLR_plot_points_bool}{\BBLR_plot_points:} \bool_if:nT {\g_BBLR_plot_lineA_bool}{ \BBLR_draw_line:NNN \g_BBLR_slope_A_fp\g_BBLR_intercept_A_fp\g_BBLR_labelA_str} \bool_if:nT {\g_BBLR_plot_lineB_bool}{ \BBLR_draw_line:NNN \g_BBLR_slope_B_fp\g_BBLR_intercept_B_fp\g_BBLR_labelB_str} \bool_if:nT {\g_BBLR_plot_lineS_bool}{ \BBLR_draw_line:NNN \g_BBLR_slope_S_fp\g_BBLR_intercept_S_fp\g_BBLR_labelS_str} \end{picture} }% % \end{macrocode} % \end{macro} % % \subsection{Functions for internal use} % The functions listed here after are for internal % use and are just minimally documented. \par % The function |\BBLR_decode_data:| % \marginpar{\raggedleft\texttt{ % \textbackslash{}BBLR\textunderscore{}decode\textunderscore{}data:}} % extract two numeric values from the string read from the file. % Some tricky actions are necessary because % a so called csv file sometime do not contains the separating commas. % \begin{macrocode} \cs_new_protected:Nn \BBLR_decode_data: { \tl_trim_spaces:N \g_BBLR_file_line_tl \int_gzero:N \g_tmpa_int \int_gzero:N \g_BBLR_first_separator_int \int_gzero:N \g_BBLR_last_separator_int \int_gset:Nn \g_BBLR_record_length_int { \str_count:N \g_BBLR_file_line_tl} \str_map_variable:NNn \g_BBLR_file_line_tl \g_tmpa_str { \int_gincr:N \g_tmpa_int \bool_lazy_or:nnTF {\str_if_eq_p:NN \g_tmpa_str \c_BBLR_comma_str} {\str_if_eq_p:NN \g_tmpa_str \c_BBLR_space_str} {\int_gset_eq:NN \g_BBLR_last_separator_int \g_tmpa_int \int_if_zero:nTF {\g_BBLR_first_separator_int} {\int_gset_eq:NN \g_BBLR_first_separator_int \g_tmpa_int }{\prg_do_nothing:} }{\prg_do_nothing:} } \int_gincr:N \g_BBLR_last_separator_int \int_gdecr:N \g_BBLR_first_separator_int \fp_gset:Nn \g_BBLR_abscissa_fp{ \str_range:Nnn \g_BBLR_file_line_tl{1}{\g_BBLR_first_separator_int}} \fp_gset:Nn \g_BBLR_ordinate_fp{ \str_range:Nnn \g_BBLR_file_line_tl {\g_BBLR_last_separator_int}{\g_BBLR_record_length_int}} } % \end{macrocode} % The function |\BBLR_plot_points:| \marginpar{\raggedleft\texttt{ % \textbackslash{}BBLR\textunderscore{}plot\textunderscore{}points:}} % scans the data file to read the coordinates and % it draws a circle for each point. % % \begin{macrocode} \cs_new_protected:Nn \BBLR_plot_points: { \ior_open:Nn \g_BBLR_file_ior \g_BBLR_file_name_tl \int_zero:N \g_BBLR_rec_count_int \int_do_until:nn {\g_BBLR_rec_count_int = \g_BBLR_number_of_points_int} { \ior_str_get:NN \g_BBLR_file_ior \g_BBLR_file_line_tl \int_incr:N \g_BBLR_rec_count_int \BBLR_decode_data: \fp_gset:Nn \g_tmpa_fp{(\g_BBLR_abscissa_fp-\g_BBLR_min_abscissa_fp)* \g_BBLR_scale_factor_fp} \fp_gset:Nn \g_tmpb_fp{(\g_BBLR_ordinate_fp-\g_BBLR_min_ordinate_fp)* \g_BBLR_scale_factor_fp} \put(\fp_use:N\g_tmpa_fp, \fp_use:N\g_tmpb_fp){ {\circle*{\fp_use:N\g_BBLR_diameter_fp}}} } \ior_close:N \g_BBLR_file_ior } % \end{macrocode} % The function |\BBLR_draw_line:NNN| \marginpar{\raggedleft\texttt{ % \textbackslash{}BBLR\textunderscore{}draw\textunderscore{}line:NNN}} % draws the line. The first two parameters given as arguments % are the slope and the intercept. The third parameter is a label. % \par The next code finds the intersection of the line with the plotting area. % \begin{macrocode} \cs_new_protected:Nn \BBLR_draw_line:NNN { \fp_gset:Nn \fp_tmpa_fp {#1 * \g_BBLR_min_abscissa_fp + #2 } \fp_compare:nTF{\fp_tmpa_fp > \g_BBLR_max_ordinate_fp}{ \fp_gset:Nn \g_BBLR_min_draw_abscissa_fp {(\g_BBLR_max_ordinate_fp -#2) / #1} }{ \fp_compare:nTF{\fp_tmpa_fp < \g_BBLR_min_ordinate_fp}{ \fp_gset:Nn \g_BBLR_min_draw_abscissa_fp {(\g_BBLR_min_ordinate_fp - #2) / #1} }{ \fp_gset:Nn \g_BBLR_min_draw_abscissa_fp { \g_BBLR_min_abscissa_fp } }} \fp_gset:Nn \fp_tmpa_fp {#1 * \g_BBLR_max_abscissa_fp + #2 } \fp_compare:nTF{\fp_tmpa_fp > \g_BBLR_max_ordinate_fp}{ \fp_gset:Nn \g_BBLR_max_draw_abscissa_fp {(\g_BBLR_max_ordinate_fp -#2) / #1} }{ \fp_compare:nTF{\fp_tmpa_fp < \g_BBLR_min_ordinate_fp}{ \fp_gset:Nn \g_BBLR_max_draw_abscissa_fp { (\g_BBLR_min_ordinate_fp - #2) / #1} }{ \fp_gset:Nn \g_BBLR_max_draw_abscissa_fp { \g_BBLR_max_abscissa_fp } }} % \end{macrocode} % Some parameters (i.e.\ starting point and base-length) % are computed and the line is drawn. % \begin{macrocode} \fp_gset:Nn \fp_tmpa_fp {(\g_BBLR_min_draw_abscissa_fp - \g_BBLR_min_abscissa_fp)* \g_BBLR_scale_factor_fp} \fp_gset:Nn \fp_tmpb_fp {(#1 * \g_BBLR_min_draw_abscissa_fp + #2 - \g_BBLR_min_ordinate_fp)* \g_BBLR_scale_factor_fp} \fp_gset:Nn \fp_BBLR_line_base_length_fp{(\g_BBLR_max_draw_abscissa_fp - \g_BBLR_min_draw_abscissa_fp) * \g_BBLR_scale_factor_fp} \put(\fp_use:N\fp_tmpa_fp, \fp_use:N\fp_tmpb_fp){ \line(1.,\fp_use:N #1){\fp_use:N\fp_BBLR_line_base_length_fp}} % \end{macrocode} %The third parameter is used as a label, if it is not a |+|. % \begin{macrocode} \bool_if:nF {\str_if_eq_p:NN #3 \c_BBLR_plus_str}{ \fp_gset:Nn \fp_tmpa_fp {0.08 * \g_BBLR_min_draw_abscissa_fp + 0.92 * \g_BBLR_max_draw_abscissa_fp} \fp_gset:Nn \fp_tmpb_fp {#1 * \fp_tmpa_fp + #2 } \fp_gset:Nn \fp_tmpa_fp {(\fp_tmpa_fp-\g_BBLR_min_abscissa_fp)*\g_BBLR_scale_factor_fp + 0.3 * #1 /sqrt(1.+#1*#1)} \fp_gset:Nn \fp_tmpb_fp {(\fp_tmpb_fp-\g_BBLR_min_ordinate_fp)* \g_BBLR_scale_factor_fp - 0.3 /sqrt(1.+#1*#1)} \put(\fp_use:N\fp_tmpa_fp, \fp_use:N\fp_tmpb_fp){#3} } } % \end{macrocode} % % \begin{macrocode} \ExplSyntaxOff % \end{macrocode} % % %\iffalse % %\fi % % \section{Acknowledgments} % The colleagues Paolo Zatelli, Alfonso Vitti and Giulia Graldi % read some preliminary version % of this text and suggested several improvements. \par % % \section{About the references} % \subsection*{Mathematics} % The books by Lang \cite{Lang} and by Strang \cite{Strang} give % all the background on linear algebra.\par % The texts by Sansò \cites{Sanso1, Sanso2} (in italian) treat the % teory of probability and its application to metrology. % See: |http://www.geolab.polimi.it/text-books/|.\par % The paper by Karl Pearson \cite{Pearson} is the oldest text that % I have found on the symmetric regression, or total regression. % \subsection*{Programming} % The two documents \cites{L3A, L3B} are the fountamental and official guide % for \LaTeX3 programming. The books by Donald Knuth \cites{Knuth} % and Leslie Lamport \cites{Lamport} are still essential references. % The papers by Enrico Gregorio \cites{egreg1, egreg2, egreg3, egreg4, egreg5} % explain some general and some special aspect of \LaTeX3 programming. % % \section{References} % \begin{biblist}[\normalsize] % \bib{egreg1}{article}{ % author={Gregorio, Enrico}, % journal={ArsTeXnica}, % number={14},pages={41\ndash 47}, date={2012}, % title={\LaTeX3: un nuovo gioco per i maghi e per diventarlo}, % } % \bib{egreg2}{article}{ % author={Gregorio, Enrico}, % journal={ArsTeXnica}, % number={22},pages={69\ndash 77}, date={2016}, % title={Liste, cicli, \LaTeX3}, % } % \bib{egreg3}{article}{ % author={Gregorio, Enrico}, % journal={ArsTeXnica}, % number={24},pages={37\ndash 44}, date={2017}, % title={Condizionali in \LaTeX}, % } % \bib{egreg4}{article}{ % author={Gregorio, Enrico}, % journal={ArsTeXnica}, % number={30},pages={36\ndash 45}, date={2020}, % title={Funzioni e |expl3|}, % } % \bib{egreg5}{article}{ % author={Gregorio, Enrico}, % journal={TUGboat}, % volume={41},number={3},pages={299\ndash 307}, date={2020}, % title={Functions and |expl3|}, % } % \bib{Knuth}{book}{ % author={Knuth, Donald}, % title={The TeXbook}, % date={1986}, % publisher={American Mathematical Society and Addison-Wesley}, % } % \bib{Lang}{book}{ % author={Lang, Serge}, % title={Linear Algebra}, % date={1987}, % publisher={Springer-Verlag}, % place={Berlin Heidelberg}, % } % \bib{Lamport}{book}{ % author={Lamport, Leslie}, % title={LaTeX - A document preparation system (2nd ed.\ )}, % date={1994}, % publisher={Addison-Wesley}, % note={something interesting in the fist edition, too}, % } % \bib{L3A}{article}{ % title={The |expl3| package and LaTeX3 programming}, % author={The LaTeX project team}, date={2024}, % note={file: |expl3.pdf| available in CTAN in l3kernel}, % } % \bib{L3B}{article}{ % title={The \LaTeX3 interface}, % author={The LaTeX project team}, date={2024}, % note={file: |interface3.pdf| available in CTAN in l3kernel} % } % \bib{Pearson}{article}{ % title={On lines and planes of closest fit to systems of points in space}, % author={Pearson, Karl}, date={1901}, % journal={Philosophical Magazine}, % volume={2},number={11},pages={559\ndash 572}, % } % \bib{Sanso1}{book}{ % author={Sansò, Fernando}, % title={Elementi di teoria della probabilità}, % date={1996}, % publisher={Città-Studi}, % place={Milano}, % } % \bib{Sanso2}{book}{ % author={Sansò, Fernando}, % title={La teoria della stima}, % date={1996}, % publisher={Città-Studi}, % place={Milano}, % } % \bib{Strang}{book}{ % author={Strang, Gilbert}, % title={Introduction to linear algebra}, % date={2009}, % publisher={Wellesley-Cambridge press,}, % } % \end{biblist} % % \par\vfill\centerline{\small ***}\vfill % \end{document} % %\iffalse % END OF FILE linearregression.dtx %\fi